diff --git a/code/scripts/predict_interventions.ipynb b/code/scripts/predict_interventions.ipynb index a45027c..ef9e260 100644 --- a/code/scripts/predict_interventions.ipynb +++ b/code/scripts/predict_interventions.ipynb @@ -1,2512 +1,4670 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predictive modelling of interventions\n", "## Ridge regression\n", "\n", "This notebook is ................\n", "\n", "\n", "First of all, we import the necessary packets." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", + "import math\n", "from sklearn import linear_model\n", "from sklearn import model_selection\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.metrics import accuracy_score\n", "\n", "# constants\n", "FIRST_COUNTRY_INDEX_WO_EXP = 12\n", "FIRST_COUNTRY_INDEX = FIRST_COUNTRY_INDEX_WO_EXP + 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prepare the dataset\n", "The data is provided in pandas dataframes. If the necessary csv files are not available, they can be generated with the script 'prepare_intervention_data.py'. This data now needs to be converted into numpy array such that we can train our model." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "9217\n", + "9218\n", "['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo Republic', 'Cook Islands', 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czech Republic', 'DR Congo', 'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'European Union', 'European Union', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait', 'Kyrgyz Republic', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico', 'Micronesia, Fed. Sts.', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Nauru', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'North Korea', 'Norway', 'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Samoa', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia', 'Solomon Islands', 'Somalia', 'South Africa', 'South Korea', 'South Sudan', 'Spain', 'Sri Lanka', 'St. Kitts and Nevis', 'St. Lucia', 'St. Vincent and the Grenadines', 'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam', 'Yemen', 'Zambia', 'Zimbabwe']\n" ] + }, + { + "data": { + "text/plain": [ + "'df = pd.DataFrame(countries)\\ndf.to_csv(\"countries.csv\")'" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(\"../data/data_regression/dataset_interventions.csv\",\n", " encoding=\"utf-8-sig\")\n", "\n", "D = 214\n", "D_wo_exp = D - 3\n", "N = len(data)\n", "print(N)\n", "dataset = np.zeros((N, D), dtype=np.float64)\n", "dataset_without_exp = np.zeros((N, D_wo_exp), dtype=np.float64)\n", "\n", "dataset_without_exp[:,:FIRST_COUNTRY_INDEX_WO_EXP] = (data.loc[:,\"year\":\"woman_proportion\"]).to_numpy()\n", "dataset[:,:FIRST_COUNTRY_INDEX] = (data.loc[:,\"year\":\"experience score parties rate\"]).to_numpy()\n", "\n", "labelset = np.zeros((N,), dtype=np.float64)\n", "labelset[:] = (data.loc[:, \"nb_interventions\"]).to_numpy()\n", "\n", "# read the valid countries\n", "country_file = open(\"../data/dictionaries/valid_countries.txt\", \"r\")\n", "countries = country_file.readlines()\n", "countries = [c.replace(\"\\n\", \"\") for c in countries]\n", "countries = sorted(countries)\n", - "print(countries)\n" + "print(countries)\n", + "\"\"\"df = pd.DataFrame(countries)\n", + "df.to_csv(\"countries.csv\")\"\"\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resting part of the dataset is the country. We need to write a function that returns the index of the country in a sorted list of the valid countries." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "def get_country_index(country):\n", " if country in countries:\n", " return countries.index(country)\n", " else:\n", " # unknown country\n", " return len(countries)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To finalize the dataset, we use the defined function. for indices 12 to 209, a 1 means that the affiliation is this country, 0 means that it isn't." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "for i in range(N):\n", " country = data.iloc[i,1]\n", " dataset_without_exp[i, FIRST_COUNTRY_INDEX_WO_EXP + get_country_index(country)] = 1\n", " dataset[i, FIRST_COUNTRY_INDEX + get_country_index(country)] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Split the data to training data and test data\n", "In a first step, I consider the first 80% of the samples as training data and the resting 20% as test data" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "# shuffle everything\n", "np.random.seed(2020)\n", "p = np.random.permutation(N)\n", "shuffled_dataset_wo_exp = dataset_without_exp[p]\n", "shuffled_dataset = dataset[p]\n", "shuffled_labelset = labelset[p]\n", "\n", "SPLIT_IDX = 7400\n", "# seperate train and test data\n", "X_train_without_exp = shuffled_dataset_wo_exp[:SPLIT_IDX]\n", "X_train = shuffled_dataset[:SPLIT_IDX]\n", "Y_train = shuffled_labelset[:SPLIT_IDX]\n", "\n", "X_test_without_exp = shuffled_dataset_wo_exp[SPLIT_IDX:]\n", "X_test = shuffled_dataset[SPLIT_IDX:]\n", "Y_test = shuffled_labelset[SPLIT_IDX:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Baseline models\n", "To have a upper bound for the performance of our models, we introduce two baseline models. The first one consists in predicting always 0 interventions." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean squared error: 91.79\n" + "RMSE: 9.54\n" ] } ], "source": [ "n = Y_test.shape\n", "test_predict_baseline_zero = np.zeros(n)\n", "baseline_zero_mse = mean_squared_error(Y_test, test_predict_baseline_zero)\n", - "print('Mean squared error: %.2f'\n", - " % baseline_zero_mse)" + "print('RMSE: %.2f'\n", + " % math.sqrt(baseline_zero_mse))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another baseline is to predict for each country the average number of interventions done in the training data meetings." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean squared error: 29.33\n" + "RMSE: 5.02\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - ":10: RuntimeWarning: invalid value encountered in double_scalars\n", + ":10: RuntimeWarning: invalid value encountered in double_scalars\n", " avg = np.sum(train_samples_this_country) / len(train_samples_this_country)\n" ] } ], "source": [ "test_predict_baseline_avg = np.zeros(n)\n", "\n", "# fill in a list with the averages\n", "intervention_averages = []\n", " \n", "# predict accordingly\n", "for i in range(len(countries)):\n", " index = FIRST_COUNTRY_INDEX + i\n", " train_samples_this_country = (Y_train[X_train[:,index] == 1])\n", " avg = np.sum(train_samples_this_country) / len(train_samples_this_country)\n", " intervention_averages.append(avg)\n", " \n", " test_predict_baseline_avg[X_test[:,index] == 1] = avg\n", " \n", "baseline_avg_mse = mean_squared_error(Y_test, test_predict_baseline_avg)\n", - "print('Mean squared error: %.2f'\n", - " % baseline_avg_mse)" + "print('RMSE: %.2f'\n", + " % math.sqrt(baseline_avg_mse))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, this result is equal to a simple linear model without global bias that only works on the country data." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean squared error: 29.33\n" + "RMSE: 5.02\n" ] } ], "source": [ "# baseline data\n", "X_train_baseline = X_train[:,FIRST_COUNTRY_INDEX:]\n", "X_test_baseline = X_test[:,FIRST_COUNTRY_INDEX:]\n", "\n", "reg = linear_model.LinearRegression(fit_intercept=False)\n", "reg.fit(X_train_baseline, Y_train)\n", "test_predict = reg.predict(X_test_baseline)\n", "intervention_mse = mean_squared_error(Y_test, test_predict)\n", - "print('Mean squared error: %.2f'\n", - " % intervention_mse)" + "print('RMSE: %.2f'\n", + " % math.sqrt(intervention_mse))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ridge regression on all the data without experience\n", "Now, we can train the actual first model, conventional ridge regression that expects a gaussion distribution. We first use crossvalidation to determine the best regularizer." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 49, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "The best regularizer is lambda = 0.43016357581067904\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# cross validation to determine regularizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mreg\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[0;36m_decomp_diag\u001b[1;34m(v_prime, Q)\u001b[0m\n\u001b[0;32m 1134\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_decomp_diag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv_prime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mQ\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1135\u001b[0m \u001b[1;31m# compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1136\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mv_prime\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mQ\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1137\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1138\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# cross validation to determine regularizer\n", - "reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000))\n", + "reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000), normalize=True)\n", "reg.fit(X_train_without_exp, Y_train)\n", "lambda_ = reg.alpha_\n", "print(f\"The best regularizer is lambda = {lambda_}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we train the model with the optimal lambda." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.1901302350423615\n", - "[-7.14120252e-02 6.60173200e-03 -3.82140800e-01 -2.46897866e-01\n", - " -5.94272034e-01 -3.33064352e-01 1.37588809e+00 6.52290537e-01\n", - " -6.00923896e-01 -2.53020482e-01 -2.25345629e-03 3.99739767e-02\n", - " -1.22227069e+00 -1.82666613e+00 -4.81319276e-01 -1.20327513e+00\n", - " -1.66545548e+00 -1.45410213e+00 2.71809137e+00 -1.70462142e+00\n", - " 1.64380064e+01 -1.59832398e+00 -1.73408519e+00 -1.63677519e+00\n", - " -1.54726449e+00 -4.41026726e-02 -9.10089342e-01 -4.68364939e-01\n", - " -1.78228197e+00 -1.49469663e+00 -1.35226888e+00 -1.36272750e+00\n", - " 3.69820465e+00 -1.47230968e+00 -1.41822641e+00 8.21725633e+00\n", - " -1.36228025e+00 -1.39434270e+00 -1.12850913e+00 -1.54448677e+00\n", - " -1.63449788e+00 -1.65991367e+00 -1.61171053e+00 1.30094802e+01\n", - " -1.32576815e+00 -1.66881530e+00 -3.27231731e-01 4.56394989e+01\n", - " 1.61297924e+00 -1.90647670e+00 -1.90866284e+00 -1.52628124e+00\n", - " -6.08536015e-01 -2.00680603e+00 -1.23532722e+00 -1.31802798e+00\n", - " -1.55475789e+00 -1.64761810e+00 -1.92414128e+00 -1.63482173e+00\n", - " -1.75047485e+00 -1.67738098e+00 -1.40897446e+00 -7.85235275e-01\n", - " 3.61868098e-01 -1.19047560e+00 -1.45979952e+00 -1.83309881e+00\n", - " -1.84686535e+00 -1.64841110e+00 -1.51138306e+00 0.00000000e+00\n", - " 0.00000000e+00 -1.52189501e+00 -1.64560317e+00 -1.05588588e+00\n", - " -1.39266183e+00 -7.90186199e-01 -1.63761675e+00 -1.06432202e+00\n", - " -4.91026168e-01 -1.82033415e+00 -5.14292152e-01 -1.18233485e+00\n", - " -1.89644777e+00 -1.79951787e+00 -1.53102507e+00 -1.61330850e+00\n", - " -1.41527559e+00 -1.07661299e+00 -5.37694838e-01 7.48095214e+00\n", - " 6.19985232e-01 1.03385441e+00 -1.24421777e+00 -1.72826381e+00\n", - " -1.73478526e+00 -1.54141921e+00 -1.41105880e+00 1.52707445e+01\n", - " -1.51358688e+00 -8.64109515e-01 -7.36153034e-01 -1.44924517e+00\n", - " 6.32904669e-01 -1.45634704e+00 -1.81806067e+00 -1.73939864e+00\n", - " -1.56573334e+00 -1.84336793e+00 -1.59140134e+00 -1.39535796e+00\n", - " -1.57873912e+00 -1.67540369e+00 -1.63775388e+00 -1.56376189e+00\n", - " -1.65660141e+00 -1.54867973e+00 8.21498673e-01 -1.39576120e+00\n", - " -1.44333079e+00 -1.65002440e+00 9.33884765e-01 -1.18392388e+00\n", - " -1.07066588e+00 1.08352942e+00 -3.59607422e-01 -1.75721231e+00\n", - " -1.75614820e+00 -1.83159110e+00 -1.46524218e+00 -1.73297949e+00\n", - " -1.66973682e+00 -1.72343421e+00 -1.41373916e+00 -1.45024701e+00\n", - " -1.25380233e+00 -1.03778672e+00 7.80945382e+00 -6.97805121e-01\n", - " -1.90689830e+00 1.00204959e-01 -1.70263005e+00 -1.68521853e+00\n", - " 7.83308085e+00 -1.12959868e+00 -1.56284587e-01 -1.56092373e+00\n", - " -9.47300680e-01 -1.16953464e+00 8.90899283e-01 -1.42552782e+00\n", - " -1.97512573e-01 2.95042516e+00 -8.29222217e-01 -1.83455820e+00\n", - " -1.04487435e+00 -1.55036868e+00 5.20847471e+00 -1.62599195e+00\n", - " 8.67960462e-02 -1.86287916e+00 -1.70351478e+00 1.31969162e+01\n", - " -7.27395236e-01 -1.52021097e+00 -1.67556876e+00 -1.37164381e+00\n", - " -3.74919190e-01 -1.73794237e+00 -1.29319626e+00 -1.54786588e+00\n", - " -1.38258546e+00 4.99885986e+00 4.94426400e-01 -1.12980470e+00\n", - " -1.22306656e+00 -1.53353193e+00 -1.64305328e+00 -1.44041827e+00\n", - " -1.69158106e+00 -1.16319553e+00 -1.62400580e+00 -1.51606916e+00\n", - " 8.63611424e+00 -1.46740249e+00 -1.40676167e+00 -2.42489283e-01\n", - " -7.25759727e-01 -1.07857631e+00 -1.77372625e+00 -1.66302307e+00\n", - " -1.13248700e+00 -1.73855687e+00 -7.69448205e-01 -1.78538213e+00\n", - " 3.89446798e+00 -3.56418948e-01 -8.55712292e-01 -1.09887879e+00\n", - " -2.06534896e+00 5.30717614e+01 -4.03126586e-01 -1.70509787e+00\n", - " -1.77945281e+00 1.31438183e+00 -1.56584114e+00 -1.69269384e+00\n", - " -1.16623707e+00 -7.98396870e-01 -2.26642611e+00]\n" - ] - } - ], + "outputs": [], "source": [ "reg = linear_model.Ridge(alpha=lambda_)\n", "reg.fit(X_train_without_exp, Y_train)\n", "w0 = reg.intercept_\n", "W = reg.coef_\n", "print(w0)\n", "print(W)\n", "np.savetxt(\"W_.csv\", W, delimiter=\",\")\n", "d = pd.DataFrame(countries)\n", "d.to_csv(\"countries.csv\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean squared error: 29.27\n" + "Mean squared error: 29.12\n" ] } ], "source": [ "test_predict = reg.predict(X_test_without_exp)\n", "print('Mean squared error: %.2f'\n", " % mean_squared_error(Y_test, test_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we notice, the mean square error is basically equal to the one in the baseline model. The additional dimensions do not help to improve the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear Model with logarithmic transformation\n", "The reason for the failure of the normal linear model is that our Y doesn't follow a Gaussian distribution, but a logarithmic. We thus try the same model but with a transformed Y. We transform the y's accordingly: \n", "$y' = log(c + y)$ with $c > 0$.\n", "We try different values for c. First, c = 1 (which preserves y = 0 to be y' = 0)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c = 0.01\n", + "Lambda is 1.1643031329208755\n", + "[ 1.89476160e-02 7.26754328e-04 5.18290112e-02 -2.19510126e-03\n", + " -1.80707670e-04 2.56762949e-02 2.43216631e+00 2.29385746e-02\n", + " 7.24287570e-02 1.78447889e-02 -1.47916071e-04 1.41220879e-03\n", + " 8.76102650e-02 8.70331237e-03 6.66821810e-02 2.67151538e-01\n", + " 1.98607083e-02 8.52368895e-03 1.29973420e-02 1.09733700e-02\n", + " 7.39755332e-01 2.14137338e-02 3.67953456e+00 -1.36859698e-03\n", + " -4.05071731e-03 1.34773006e-02 1.22010797e-02 2.99416375e-01\n", + " 9.92092858e-01 3.20856852e-02]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0. 1. 0. 2. 0.\n", + " 0. 0. 1. 7. 8. 0.]\n", + "Mean squared error : 54.46\n", + "c = 10\n", + "Lambda is 0.3643858983763548\n", + "[ 0.59162116 -0.05958835 1.20030316 -0.10378582 -0.20233178 0.50874258\n", + " 5.61529685 0.48428403 0.91213953 0.26681428 0.03383258 -0.30917334\n", + " 1.74316404 0.07476468 0.91277749 1.58081876 0.36976566 0.44955753\n", + " 0.1758825 0.15734586 4.47886173 0.44291712 13.43903104 -0.265934\n", + " -0.45540002 0.3895965 0.08881619 1.86978302 3.91830965 0.58959735]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0. 1. 0. 2. 0.\n", + " 0. 0. 1. 7. 8. 0.]\n", + "Mean squared error : 34.34\n", + "c = 1000\n", + "Lambda is 0.4070142453219439\n", + "[ 9.09533200e-01 -1.56329171e-01 1.63235499e+00 -8.59942433e-02\n", + " -4.97500617e-01 6.55202142e-01 6.63021846e+00 6.78586606e-01\n", + " 1.15680971e+00 4.18771014e-01 1.05187470e-01 -7.32043795e-01\n", + " 2.51351011e+00 2.92602698e-03 1.36675526e+00 1.56959376e+00\n", + " 4.66281746e-01 8.59666859e-01 -6.46980878e-02 -9.68650054e-02\n", + " 5.96583818e+00 4.27719765e-01 1.72811459e+01 -5.07945411e-01\n", + " -8.86352844e-01 5.92082524e-01 -1.00065970e-01 2.08072875e+00\n", + " 4.28402903e+00 8.30013514e-01]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0. 1. 0. 2. 0.\n", + " 0. 0. 1. 7. 8. 0.]\n", + "Mean squared error : 29.44\n", + "c = 2000\n", + "Lambda is 0.41842885079015846\n", + "[ 9.17241411e-01 -1.58284593e-01 1.63637662e+00 -8.41213915e-02\n", + " -5.04720239e-01 6.58956423e-01 6.64188101e+00 6.82991195e-01\n", + " 1.16176063e+00 4.26240402e-01 1.05930037e-01 -7.41989547e-01\n", + " 2.52417064e+00 2.34007791e-03 1.37831587e+00 1.56638205e+00\n", + " 4.69432051e-01 8.70779784e-01 -7.25013609e-02 -1.05775763e-01\n", + " 5.98469184e+00 4.25597561e-01 1.73313930e+01 -5.11995567e-01\n", + " -8.96030666e-01 5.97175338e-01 -1.03799423e-01 2.08168009e+00\n", + " 4.28405178e+00 8.38805074e-01]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0. 1. 0. 2. 0.\n", + " 0. 0. 1. 7. 8. 0.]\n", + "Mean squared error : 29.36\n", + "c = 5000\n", + "Lambda is 0.41842885079015846\n", + "[ 9.22012759e-01 -1.59397632e-01 1.63893823e+00 -8.36758519e-02\n", + " -5.09627006e-01 6.60932512e-01 6.64993441e+00 6.85527482e-01\n", + " 1.16518405e+00 4.30792344e-01 1.05980907e-01 -7.48385959e-01\n", + " 2.53058196e+00 1.55503697e-03 1.38472023e+00 1.56457015e+00\n", + " 4.71544050e-01 8.75993182e-01 -7.72584329e-02 -1.11336048e-01\n", + " 5.99687640e+00 4.23973530e-01 1.73641200e+01 -5.14582910e-01\n", + " -9.02341443e-01 6.00154151e-01 -1.06537262e-01 2.08234559e+00\n", + " 4.28424531e+00 8.43895989e-01]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0. 1. 0. 2. 0.\n", + " 0. 0. 1. 7. 8. 0.]\n", + "Mean squared error : 29.30\n", + "c = 10000\n", + "Lambda is 0.43016357581067904\n", + "[ 9.23833034e-01 -1.59950517e-01 1.63958923e+00 -8.23212923e-02\n", + " -5.10779392e-01 6.62292479e-01 6.65127213e+00 6.86797635e-01\n", + " 1.16599332e+00 4.32783115e-01 1.06526854e-01 -7.50393730e-01\n", + " 2.53301038e+00 2.00977927e-03 1.38831769e+00 1.56357915e+00\n", + " 4.72141251e-01 8.80672096e-01 -7.92560386e-02 -1.13457408e-01\n", + " 5.99997619e+00 4.23796959e-01 1.73719368e+01 -5.15349322e-01\n", + " -9.04182455e-01 6.01587126e-01 -1.06835403e-01 2.08241414e+00\n", + " 4.28379110e+00 8.46559915e-01]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0. 1. 0. 2. 0.\n", + " 0. 0. 1. 7. 8. 0.]\n", + "Mean squared error : 29.29\n", + "c = 20000\n", + "Lambda is 0.43016357581067904\n", + "[ 9.24660635e-01 -1.60141210e-01 1.64001948e+00 -8.22443567e-02\n", + " -5.11630981e-01 6.62637189e-01 6.65264509e+00 6.87236790e-01\n", + " 1.16659441e+00 4.33581199e-01 1.06524903e-01 -7.51500696e-01\n", + " 2.53409962e+00 1.87733626e-03 1.38942143e+00 1.56326477e+00\n", + " 4.72510435e-01 8.81570806e-01 -8.00854470e-02 -1.14426648e-01\n", + " 6.00204143e+00 4.23510184e-01 1.73774760e+01 -5.15791968e-01\n", + " -9.05274534e-01 6.02104331e-01 -1.07307795e-01 2.08252585e+00\n", + " 4.28380890e+00 8.47452427e-01]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0. 1. 0. 2. 0.\n", + " 0. 0. 1. 7. 8. 0.]\n", + "Mean squared error : 29.28\n" + ] + } + ], "source": [ "cs = [0.01, 10, 1000, 2000, 5000, 10000, 20000]\n", "\n", "for c in cs:\n", " print(f\"c = {c}\")\n", " # logarithmic transformation\n", " Y_train_transf = np.log(c + Y_train)\n", " Y_test_transf = np.log(c + Y_test)\n", "\n", " # crossvalidation for lambda\n", " reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000))\n", " reg.fit(X_train_without_exp, Y_train_transf)\n", " lambda_ = reg.alpha_\n", " print(f\"Lambda is {lambda_}\")\n", " test_predict_transf = reg.predict(X_test_without_exp)\n", " # transform the output back\n", " test_predict = np.exp(test_predict_transf) - c\n", " \n", " #print(test_predict_transf)\n", " print(test_predict[:30])\n", " #print(Y_test_transf)\n", " print(Y_test[:30])\n", "\n", " print('Mean squared error : %.2f'\n", " % mean_squared_error(Y_test, test_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compare, we do the baseline data (only countries) with the optimal c that we found" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean squared error : 44.81\n" ] } ], "source": [ "c = 0.3 # the optimal found above\n", "\n", "# logarithmic transformation\n", "Y_train_transf = np.log(c + Y_train)\n", "Y_test_transf = np.log(c + Y_test)\n", "\n", "# crossvalidation for lambda\n", "reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000))\n", "reg.fit(X_train_baseline, Y_train_transf)\n", "lambda_ = reg.alpha_\n", "test_predict_transf = reg.predict(X_test_baseline)\n", "# transform the output back\n", "test_predict = np.exp(test_predict_transf) - c\n", "\n", "print('Mean squared error : %.2f'\n", " % mean_squared_error(Y_test, test_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combine multiple models\n", "Even with the logistic transformation, our data doesn't follow a Gaussian distribution. One problem is that there are a lot of samples with value 0. Several papers suggest for situations like that to first perform a classifying task that decides whether a sample is 0 or not, and then apply a second model. (e.g. https://www.kent.ac.uk/smsas/personal/msr/webfiles/zip/ibc_fin.pdf) As we work with count data, Poisson distribution may fit our data better.\\\n", "Hence, we first apply logistic regression to classify into 0 and non 0." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'C': 1.873817422860385}\n", "0.7501375894331315\n", "0.7006053935057788\n", "accuracy = 0.7985690698954321\n" ] } ], "source": [ "Y_train_class = Y_train > 0\n", "Y_test_class = Y_test > 0\n", "\n", "clf = linear_model.LogisticRegression(max_iter=2000, fit_intercept=True)\n", "\n", "# do crossvalidation\n", "params = {'C': np.logspace(-3, 3, 100)}\n", "cv = model_selection.GridSearchCV(clf, params)\n", "cv.fit(X_train_without_exp, Y_train_class)\n", "print(cv.best_params_)\n", "\n", "predict_class = cv.predict(X_test_without_exp)\n", "print(1 - np.mean(predict_class))\n", "print(1 - np.mean(Y_test_class))\n", "accuracy = accuracy_score(Y_test_class, predict_class)\n", "print(f\"accuracy = {accuracy}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Note: only 78.3% accuracy on training data.)\\\n", "Now, on the data that is not zero, we apply a Poission regressor." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\link.py:90: RuntimeWarning: overflow encountered in exp\n", " return np.exp(lin_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\link.py:93: RuntimeWarning: overflow encountered in exp\n", " return np.exp(lin_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:132: RuntimeWarning: overflow encountered in multiply\n", " return -2 * (y - y_pred) / self.unit_variance(y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:132: RuntimeWarning: invalid value encountered in true_divide\n", " return -2 * (y - y_pred) / self.unit_variance(y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:315: RuntimeWarning: invalid value encountered in add\n", " dev = 2 * (xlogy(y, y/y_pred) - y + y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:315: RuntimeWarning: overflow encountered in multiply\n", " dev = 2 * (xlogy(y, y/y_pred) - y + y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:132: RuntimeWarning: divide by zero encountered in true_divide\n", " return -2 * (y - y_pred) / self.unit_variance(y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:132: RuntimeWarning: overflow encountered in true_divide\n", " return -2 * (y - y_pred) / self.unit_variance(y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:43: RuntimeWarning: invalid value encountered in multiply\n", " temp = d1 * family.deviance_derivative(y, y_pred, weights)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:45: RuntimeWarning: invalid value encountered in matmul\n", " devp = np.concatenate(([temp.sum()], temp @ X))\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:315: RuntimeWarning: divide by zero encountered in true_divide\n", " dev = 2 * (xlogy(y, y/y_pred) - y + y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\_loss\\glm_distribution.py:315: RuntimeWarning: overflow encountered in true_divide\n", " dev = 2 * (xlogy(y, y/y_pred) - y + y_pred)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=2):\n", "ABNORMAL_TERMINATION_IN_LNSRCH.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0020565123083486517}\n", + "Mean squared error on Poisson data : 3329138595201241213538930654784107077917171067397634262681151721437578625361621285520216433459467533110214656.00\n", + "[ 3.03428793e+02 -9.90000000e+01 -9.72817182e+01 -9.26109439e+01\n", + " 9.96633158e+02 2.88095799e+03 -7.99144631e+01 3.49342711e+19\n", + " 3.26891737e+06 3.26891737e+06 -9.90000000e+01 -7.99144631e+01\n", + " -9.90000000e+01 -9.72817182e+01 -9.90000000e+01 -9.90000000e+01\n", + " 3.26891737e+06 9.96633158e+02 -9.90000000e+01 2.19264658e+04]\n", + "[ 9.1911159 2.30401065 9.55377012 16.07243718 2.93712332 5.06174322\n", + " 7.17567978 51.94484818 20.49297106 4.80726118 2.43785712 2.46140863\n", + " 1.80906128 2.38309893 3.33890445 14.863487 12.9832018 2.70155751\n", + " 2.08227577 2.06505274]\n", + "[False False False False False False True False False False False False\n", + " False False False True False False False False]\n", + "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0.]\n", + "[0. 0. 0. 0. 0. 0.\n", + " 9.1911159 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 2.30401065 0. 0.\n", + " 0. 0. ]\n", + "Mean squared error : 28.13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] } ], "source": [ "\"\"\"X_train_poiss = X_train[clf.predict(X_train) == 1]\n", "Y_train_poiss = Y_train[clf.predict(X_train) == 1]\n", "X_test_poiss = X_test[clf.predict(X_test) == 1]\n", "Y_test_poiss = Y_test[clf.predict(X_test) == 1]\"\"\"\n", "X_train_poiss = X_train_without_exp[Y_train_class == 1]\n", "Y_train_poiss = Y_train[Y_train_class == 1]\n", "X_test_poiss = X_test_without_exp[cv.predict(X_test_without_exp) == 1]\n", "Y_test_poiss = Y_test[cv.predict(X_test_without_exp) == 1]\n", "\n", "# log transformation\n", "c = 100 # TODO try others\n", "#Y_train_poiss = np.log(c + Y_train_poiss)\n", "#Y_test_poiss = np.log(c + Y_test_poiss)\n", "\n", "params = {'alpha': np.logspace(-4, 1, 100)}\n", "reg_base = linear_model.PoissonRegressor(max_iter=1000)\n", "reg = model_selection.GridSearchCV(reg_base, params)\n", "# reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000))\n", "reg.fit(X_train_poiss, Y_train_poiss)\n", "print(reg.best_params_)\n", "predict_poiss = reg.predict(X_test_poiss)\n", "\n", "#predict_poiss = np.exp(predict_poiss) - c\n", "print('Mean squared error on Poisson data : %.2f'\n", " % mean_squared_error(np.exp(Y_test_poiss) - c, predict_poiss))\n", - "print((np.exp(Y_test_poiss) - c)[:20])\n", + "print(Y_test_poiss[:20])\n", "print(predict_poiss[:20])\n", "\n", "# on everything\n", "predict = np.zeros(Y_test.shape)\n", "print(cv.predict(X_test_without_exp)[:20])\n", "print(Y_test[:20])\n", "predict[cv.predict(X_test_without_exp) == 1] = predict_poiss\n", "print(predict[:20])\n", "\n", "print('Mean squared error : %.2f'\n", " % mean_squared_error(Y_test, predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Models with experience data\n", "We now build the same models but including the data about the experience of participants.\n", "\n", "First we build a trivial linear model." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 89, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The best regularizer is lambda = 0.43016357581067904\n" + "The best regularizer is lambda = 0.01\n" ] } ], "source": [ "# cross validation to determine regularizer\n", - "reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000))\n", + "reg = linear_model.RidgeCV(normalize=True, alphas=np.logspace(-6, 6, 1000))\n", "reg.fit(X_train, Y_train)\n", "lambda_ = reg.alpha_\n", "print(f\"The best regularizer is lambda = {lambda_}\")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 90, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.714562636822776\n", - "[-8.42740280e-02 6.25202489e-03 -3.97876267e-01 -2.93231523e-01\n", - " -5.14868906e-01 -3.73958354e-01 1.34920496e+00 6.49909570e-01\n", - " -5.43697937e-01 -2.73357809e-01 -2.59025366e-03 9.49772406e-02\n", - " 6.47481292e-02 -4.74598407e-02 6.65238396e-01 -9.75729925e-01\n", - " -1.79266387e+00 -5.02591910e-01 -9.84230361e-01 -1.67043563e+00\n", - " -1.52479569e+00 2.69375962e+00 -1.81358927e+00 1.64418397e+01\n", - " -1.68908592e+00 -1.57797100e+00 -1.74432568e+00 -1.33684674e+00\n", - " 1.20970239e-01 -9.15636708e-01 -3.73511731e-01 -1.88656071e+00\n", - " -1.50230637e+00 -1.40210520e+00 -1.41756276e+00 3.75832932e+00\n", - " -1.51804693e+00 -1.45012134e+00 8.09619713e+00 -1.27895426e+00\n", - " -1.37495790e+00 -1.12157053e+00 -1.53314379e+00 -1.56432065e+00\n", - " -1.68975876e+00 -1.54085838e+00 1.30082001e+01 -1.27179105e+00\n", - " -1.74863944e+00 -3.35535110e-01 4.55224333e+01 1.63400780e+00\n", - " -1.89964038e+00 -1.94438671e+00 -1.52883914e+00 -6.24845984e-01\n", - " -2.08660149e+00 -1.24560424e+00 -1.34991799e+00 -1.65852561e+00\n", - " -1.69334413e+00 -1.93723036e+00 -1.68543213e+00 -1.77529513e+00\n", - " -1.59457133e+00 -1.39535678e+00 -6.86861413e-01 4.09988631e-01\n", - " -1.10503374e+00 -1.28852895e+00 -1.71304084e+00 -1.89723502e+00\n", - " -1.65168958e+00 -1.45686586e+00 -1.45339301e+00 -1.77245492e+00\n", - " -1.16408930e+00 -1.36566613e+00 -7.84256269e-01 -1.63106818e+00\n", - " -1.09980692e+00 -4.83846117e-01 -1.87408892e+00 -4.36841844e-01\n", - " -1.23769530e+00 -1.92533359e+00 -1.83846185e+00 -1.33700719e+00\n", - " -1.60267855e+00 -1.33386012e+00 -1.05383924e+00 -5.69336458e-01\n", - " 7.49910832e+00 5.37514672e-01 1.08236937e+00 -1.12133728e+00\n", - " -1.77822206e+00 -1.70060854e+00 -1.66026305e+00 -1.36826133e+00\n", - " 1.52645837e+01 -1.45017861e+00 -7.90517561e-01 -7.48708533e-01\n", - " -1.38161557e+00 6.18930599e-01 -1.40373178e+00 -1.86129296e+00\n", - " -1.76046894e+00 -1.54644460e+00 -1.82753573e+00 -1.53318533e+00\n", - " -1.28835639e+00 -1.68456315e+00 -1.64964844e+00 -1.68735017e+00\n", - " -1.39610882e+00 -1.64728863e+00 -1.48991943e+00 8.12440384e-01\n", - " -1.44328630e+00 -1.49110910e+00 -1.60726979e+00 9.68081106e-01\n", - " -1.16774895e+00 -1.05966482e+00 1.04529681e+00 -4.24174998e-01\n", - " -1.84058382e+00 -1.74640645e+00 -1.70424572e+00 -1.35619692e+00\n", - " -1.77555080e+00 -1.66107081e+00 -1.46161069e+00 -1.45756560e+00\n", - " -1.42398724e+00 -1.14040693e+00 -1.02721690e+00 7.78278659e+00\n", - " -6.42989395e-01 -1.94400679e+00 1.50227193e-02 -1.62497935e+00\n", - " -1.36983621e+00 7.74937266e+00 -1.06304981e+00 -4.56532709e-02\n", - " -1.44252989e+00 -5.71087078e-01 -1.19374392e+00 9.20164317e-01\n", - " -1.34127353e+00 -1.57390571e-01 2.94659308e+00 -8.78456301e-01\n", - " -1.87775518e+00 -9.89449966e-01 -1.56398070e+00 5.20976872e+00\n", - " -1.64314723e+00 6.16255646e-02 -1.64255155e+00 -1.72854832e+00\n", - " 1.31645249e+01 -8.46271225e-01 -1.50611094e+00 -1.69595500e+00\n", - " -1.31357816e+00 -3.99542586e-01 -1.75441370e+00 -1.30883835e+00\n", - " -1.59897455e+00 -9.44741237e-01 5.04731873e+00 4.76013911e-01\n", - " -8.95775573e-01 -1.26473470e+00 -1.38225356e+00 -1.71136052e+00\n", - " -1.44632041e+00 -1.61973937e+00 -1.13417767e+00 -1.46969202e+00\n", - " -1.59238051e+00 8.55055036e+00 -1.28451181e+00 -1.38566326e+00\n", - " -2.36413389e-01 -7.84661690e-01 -1.04929789e+00 -1.76424387e+00\n", - " -1.47501580e+00 -1.16792986e+00 -1.83214956e+00 -6.15581520e-01\n", - " -1.72389105e+00 3.83624284e+00 -4.37055990e-01 -8.05683577e-01\n", - " -1.04551526e+00 -2.11674385e+00 5.29749490e+01 -4.52297529e-01\n", - " -1.69245118e+00 -1.71756198e+00 -1.63084216e+00 1.30245117e+00\n", - " -1.59072302e+00 -1.62281991e+00 -1.13137294e+00 -8.85130642e-01\n", - " -2.03543256e+00 -2.17923487e+00]\n" + "3.2816853823678347\n" ] } ], "source": [ "reg = linear_model.Ridge(alpha=lambda_)\n", "reg.fit(X_train, Y_train)\n", "w0 = reg.intercept_\n", "W = reg.coef_\n", "print(w0)\n", - "print(W)\n", "np.savetxt(\"W_withexp.csv\", W, delimiter=\",\")" ] }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 1.18297512e+00 -7.42407867e-01 1.65214044e+00 -2.46034308e-02\n", - " -4.69021814e-01 8.68726653e-01 6.72104627e+00 8.29711110e-01\n", - " 1.30591417e+00 3.87780478e-01 2.73052330e-02 -8.52067029e-01\n", - " 2.42915316e+00 6.22925442e-03 9.82701624e-01 1.85688847e+00\n", - " 5.14099547e-01 6.61749796e-01 -1.34684735e-01 -1.17630981e-01]\n", - "[0. 0. 1. 0. 0. 1. 6. 0. 4. 0. 0. 1. 2. 0. 5. 0. 1. 0. 0. 0.]\n", - "Mean squared error: 29.32\n" + "[-0.88747834 0.75475543 -1.73102741 0.12061824 3.15932883 -0.61007667\n", + " 0.28728488 0.57753117 1.39473285 1.31653016 0.0864294 0.37092249\n", + " -0.34985169 6.55224958 0.33655425 0.93739429 0.87347169 0.31787916\n", + " 0.68421022 0.40678235]\n", + "[0. 0. 0. 0. 1. 0. 0. 0. 2. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + "RMSE: 5.01\n" ] } ], "source": [ "test_predict = reg.predict(X_test)\n", "print(test_predict[:20])\n", "print(Y_test[:20])\n", - "print('Mean squared error: %.2f'\n", - " % mean_squared_error(Y_test, test_predict))" + "print('RMSE: %.2f'\n", + " % math.sqrt(mean_squared_error(Y_test, test_predict)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second, we do the linear model with a logistic transformation." ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c = 10\n", "Lambda: 0.3544455673970436\n", "[2.36868331 2.27255708 2.41760197 ... 2.97955573 2.31652432 3.87262415]\n", "[2.30258509 2.30258509 2.39789527 ... 2.30258509 2.30258509 3.8501476 ]\n", "[ 0.68331637 -0.29581649 1.21892376 ... 9.67907192 0.14036829\n", " 38.06835913]\n", "[ 0. 0. 1. ... 0. 0. 37.]\n", "sanity\n", "[[23. 10. 0. ... 0. 0. 0.]\n", " [15. 2. 1. ... 0. 0. 0.]\n", " [14. 13. 0. ... 0. 0. 0.]\n", " ...\n", " [21. 11. 1. ... 0. 0. 0.]\n", " [18. 10. 0. ... 0. 0. 0.]\n", " [24. 6. 1. ... 0. 0. 0.]]\n", "Mean squared error : 34.32\n", "c = 100\n", "Lambda: 0.3643858983763548\n", "[4.61541371 4.59918935 4.62094193 ... 4.70766727 4.60605685 4.97402359]\n", "[4.60517019 4.60517019 4.61512052 ... 4.60517019 4.60517019 4.91998093]\n", "[ 1.02961728 -0.59629879 1.58967752 ... 10.79340722 0.08870552\n", " 44.60755927]\n", "[ 0. 0. 1. ... 0. 0. 37.]\n", "sanity\n", "[[23. 10. 0. ... 0. 0. 0.]\n", " [15. 2. 1. ... 0. 0. 0.]\n", " [14. 13. 0. ... 0. 0. 0.]\n", " ...\n", " [21. 11. 1. ... 0. 0. 0.]\n", " [18. 10. 0. ... 0. 0. 0.]\n", " [24. 6. 1. ... 0. 0. 0.]]\n", "Mean squared error : 30.59\n", "c = 500\n", "Lambda: 0.4070142453219439\n", "[6.2168895 6.21320115 6.21788416 ... 6.23641285 6.21472897 6.30389222]\n", "[6.2146081 6.2146081 6.2166061 ... 6.2146081 6.2146081 6.28599809]\n", "[ 1.14200538 -0.70298117 1.64071919 ... 11.02210731 0.06044109\n", " 46.69563152]\n", "[ 0. 0. 1. ... 0. 0. 37.]\n", "sanity\n", "[[23. 10. 0. ... 0. 0. 0.]\n", " [15. 2. 1. ... 0. 0. 0.]\n", " [14. 13. 0. ... 0. 0. 0.]\n", " ...\n", " [21. 11. 1. ... 0. 0. 0.]\n", " [18. 10. 0. ... 0. 0. 0.]\n", " [24. 6. 1. ... 0. 0. 0.]]\n", "Mean squared error : 29.64\n", "c = 1000\n", "Lambda: 0.41842885079015846\n", "[6.90891612 6.90703328 6.90940047 ... 6.91874894 6.90781098 6.95374095]\n", "[6.90775528 6.90775528 6.90875478 ... 6.90775528 6.90775528 6.94408721]\n", "[ 1.16151439 -0.72173879 1.64655001 ... 11.05431636 0.05569874\n", " 47.0594116 ]\n", "[ 0. 0. 1. ... 0. 0. 37.]\n", "sanity\n", "[[23. 10. 0. ... 0. 0. 0.]\n", " [15. 2. 1. ... 0. 0. 0.]\n", " [14. 13. 0. ... 0. 0. 0.]\n", " ...\n", " [21. 11. 1. ... 0. 0. 0.]\n", " [18. 10. 0. ... 0. 0. 0.]\n", " [24. 6. 1. ... 0. 0. 0.]]\n", "Mean squared error : 29.49\n" ] } ], "source": [ "cs = [10, 100, 500, 1000]\n", "\n", "for c in cs:\n", " print(f\"c = {c}\")\n", " # logarithmic transformation\n", " Y_train_transf = np.log(c + Y_train)\n", " Y_test_transf = np.log(c + Y_test)\n", " \n", " # crossvalidation for lambda\n", " reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000))\n", " reg.fit(X_train, Y_train_transf)\n", " print(f\"Lambda: {reg.alpha_}\")\n", " test_predict_transf = reg.predict(X_test)\n", " # transform the output back\n", " test_predict = np.exp(test_predict_transf) - c\n", " \n", " print(test_predict_transf)\n", " print(Y_test_transf)\n", " print(test_predict)\n", " print(Y_test)\n", " \n", - " print('Mean squared error : %.2f'\n", - " % mean_squared_error(Y_test, test_predict))" + " print('RMSE : %.2f'\n", + " % math.sqrt(mean_squared_error(Y_test, test_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last, we apply the two step model with Logistic Regression and the Poisson Regressor." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'C': 10.0}\n", + "0.7618261826182618\n", + "0.6947194719471947\n", + "accuracy = 0.7898789878987899\n" + ] + } + ], + "source": [ + "Y_train_class = Y_train > 0\n", + "Y_test_class = Y_test > 0\n", + "\n", + "clf = linear_model.LogisticRegression(max_iter=2000, fit_intercept=True)\n", + "\n", + "# do crossvalidation\n", + "params = {'C': np.logspace(-3, 3, 100)}\n", + "cv = model_selection.GridSearchCV(clf, params)\n", + "cv.fit(X_train, Y_train_class)\n", + "print(cv.best_params_)\n", + "\n", + "predict_class = cv.predict(X_test)\n", + "print(1 - np.mean(predict_class))\n", + "print(1 - np.mean(Y_test_class))\n", + "accuracy = accuracy_score(Y_test_class, predict_class)\n", + "print(f\"accuracy = {accuracy}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n", + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0009111627561154887}\n", + "RMSE on Poisson data : 9.72\n", + "[ 1. 1. 40. 75. 5. 0. 0. 3. 1. 0. 46. 8. 49. 5. 0. 0. 0. 5.\n", + " 40. 0.]\n", + "[ 4.70812541 8.46482432 20.18096462 80.16440591 5.77702507 5.13370336\n", + " 2.49219488 6.81159046 2.40272153 2.50801772 61.09223957 6.66575914\n", + " 63.11790716 6.25087986 2.09003041 2.07205881 6.65963302 13.42323688\n", + " 22.85311783 12.82629784]\n", + "[False False False False True False False False False False False False\n", + " False True False False False False False False]\n", + "[0. 0. 0. 0. 1. 0. 0. 0. 2. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n", + "[0. 0. 0. 0. 4.70812541 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 8.46482432 0. 0. 0. 0.\n", + " 0. 0. ]\n", + "RMSE : 4.93\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_glm\\glm.py:285: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + } + ], + "source": [ + "X_train_poiss = X_train[Y_train_class == 1]\n", + "Y_train_poiss = Y_train[Y_train_class == 1]\n", + "X_test_poiss = X_test[cv.predict(X_test) == 1]\n", + "Y_test_poiss = Y_test[cv.predict(X_test) == 1]\n", + "\n", + "# log transformation\n", + "c = 100 # TODO try others\n", + "#Y_train_poiss = np.log(c + Y_train_poiss)\n", + "#Y_test_poiss = np.log(c + Y_test_poiss)\n", + "\n", + "params = {'alpha': np.logspace(-4, 1, 100)}\n", + "reg_base = linear_model.PoissonRegressor(max_iter=1000)\n", + "reg = model_selection.GridSearchCV(reg_base, params)\n", + "# reg = linear_model.RidgeCV(alphas=np.logspace(-6, 6, 1000))\n", + "reg.fit(X_train_poiss, Y_train_poiss)\n", + "print(reg.best_params_)\n", + "predict_poiss = reg.predict(X_test_poiss)\n", + "\n", + "#predict_poiss = np.exp(predict_poiss) - c\n", + "print('RMSE on Poisson data : %.2f'\n", + " % math.sqrt(mean_squared_error(Y_test_poiss, predict_poiss)))\n", + "print(Y_test_poiss[:20])\n", + "print(predict_poiss[:20])\n", + "\n", + "# on everything\n", + "predict_tot = np.zeros(Y_test.shape)\n", + "print(cv.predict(X_test)[:20])\n", + "print(Y_test[:20])\n", + "predict_tot[cv.predict(X_test) == 1] = predict_poiss\n", + "print(predict_tot[:20])\n", + "\n", + "print('RMSE : %.2f'\n", + " % math.sqrt(mean_squared_error(Y_test, predict_tot)))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE : 5.40\n" + ] + } + ], + "source": [ + "print('RMSE : %.2f'\n", + " % math.sqrt(mean_squared_error(Y_test, predict_tot)))" + ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 } diff --git a/code/scripts/prepare_intervention_data.py b/code/scripts/prepare_intervention_data.py index a347ba3..bfe8ab0 100644 --- a/code/scripts/prepare_intervention_data.py +++ b/code/scripts/prepare_intervention_data.py @@ -1,171 +1,171 @@ """ Prepare the intervention data for predictive modelling """ import pandas as pd import country_converter as coco import plots.plot_government as plot_government import plots.plot_experience as plot_experience import plots.plot_gender_rate as plot_gender_rate import find_experience as exp metadata = pd.read_csv("../data/meetings_metadata.csv") YEAR_OF_FIRST_MEETING = metadata["year"].iloc[0] issue_list = pd.read_csv("../data/data_tatiana/list_meetings.csv") issue_dict = {} -countries_dict = {"european union": "european union"} +countries_dict = {"european union": "European Union", "EU": "European Union", "UK": "United Kingdom"} def is_processed_meeting(issue_number): meeting_label = get_meeting_label(issue_number) return meeting_label in set(metadata["label"]) def get_meeting_label(issue_number): if issue_number in issue_dict: return issue_dict[issue_number] else: meeting = issue_list.loc[issue_list["issue_number"] == issue_number] label = (str(meeting.iloc[0, 0])).lower() + str(meeting.iloc[0, 1]) issue_dict[issue_number] = label return label def convert_country_names(country): if country in countries_dict: return countries_dict[country] else: converted = coco.convert(names=[(str(country)).lower()], to="name_short", not_found="None") if isinstance(converted, list): converted = converted[0] countries_dict[country] = converted return converted # prepare Tatianas data def prepare_intervention_data(): intervention_data = pd.read_csv("../data/data_tatiana/interventions.csv") prepared_data = pd.DataFrame(columns={"meeting", "country", "nb_interventions"}) intervention_data = intervention_data.loc[intervention_data.issue_number.apply(is_processed_meeting)] intervention_data["entity"] = intervention_data.entity.apply(convert_country_names) intervention_data = intervention_data.loc[intervention_data["entity"] != "None"] prepared_data["meeting"] = intervention_data["issue_number"].apply(get_meeting_label) prepared_data["country"] = intervention_data["entity"] prepared_data["nb_interventions"] = intervention_data["interventions"] prepared_data.to_csv("../data/data_regression/interventions_prepared.csv", encoding="utf-8-sig", index=False) # Prepare my data def prepare_affiliation_data(): complete_data = pd.read_csv("../results/complete_dataset.csv", encoding="utf-8-sig") complete_data_with_experience = pd.read_csv("../results/complete_dataset_experience-def.csv", encoding="utf-8-sig") parties = complete_data.loc[complete_data["affiliation_category"] == "parties"] parties_with_experience = complete_data_with_experience.loc[complete_data_with_experience["affiliation_category"] == "parties"] prepared_data = pd.DataFrame(columns={"meeting", "country", "year", "nb_delegates", "meeting_type", "government_rate", "diplomacy_rate", "security_rate", "press_rate", "no_description_rate", "no_role_rate", "nb_fossil_fuel_industry", "woman_proportion", "experience score"}) for index, row in metadata.iterrows(): label = row["label"] abs_year = row["year"] year = abs_year - YEAR_OF_FIRST_MEETING this_meeting = parties_with_experience.loc[parties_with_experience["meeting"] == label] by_country = this_meeting.groupby("affiliation") for aff, people in by_country: meeting_type = 0 if label.startswith("sb"): meeting_type = 1 gov, dipl, sec, press, uni, nodescr, nokeyword = plot_government.get_roles(people) """experience_score = plot_experience.get_experience_score(list(people["experience"]))""" experience_score_cop = exp.get_experience_score(list(people["experience cop"])) experience_score_sb = exp.get_experience_score(list(people["experience sb"])) experience_score_party_rate = (0 if experience_score_cop == 0 and experience_score_sb == 0 else exp.get_experience_score(list(people["experience party"])) / (experience_score_cop + experience_score_sb)) # TODO does groupby work on a people object? woman_proportion = plot_gender_rate.get_women_proportion(people) # fossil fuel industry (not in the experience plot) fossil_fuel_related = people[people.fossil_fuel_industry == 1] nb_fossil_fuel_delegates = len(fossil_fuel_related) """nb_fossil_fuel_delegates = 0""" prepared_data = prepared_data.append({ "meeting": label, "country": aff, "year": year, "nb_delegates": len(people), "meeting_type": meeting_type, "government_rate": gov, "diplomacy_rate": dipl, "security_rate": sec, "press_rate": press, "university_rate": uni, "no_description_rate": nodescr, "no_role_rate": nokeyword, "nb_fossil_fuel_industry": nb_fossil_fuel_delegates, "woman_proportion": woman_proportion, "experience score cop": experience_score_cop, "experience score sb": experience_score_sb, "experience score parties rate": experience_score_party_rate }, ignore_index=True) # fix order and store it prepared_data = prepared_data[ ["meeting", "country", "year", "nb_delegates", "meeting_type", "government_rate", "diplomacy_rate", "security_rate", "press_rate", "university_rate", "no_description_rate", "no_role_rate", "nb_fossil_fuel_industry", "woman_proportion", "experience score cop", "experience score sb", "experience score parties rate"]] prepared_data.to_csv("../data/data_regression/interventions_prepared_aff.csv", encoding="utf-8-sig", index=False) def fuse_datasets(): intervention_data = pd.read_csv("../data/data_regression/interventions_prepared.csv", encoding="utf-8-sig") affiliation_data = pd.read_csv("../data/data_regression/interventions_prepared_aff.csv", encoding="utf-8-sig") entire_dataset = pd.DataFrame(columns={"meeting", "country", "nb_delegates", "meeting_type", "government_rate", "diplomacy_rate", "security_rate", "press_rate", "university_rate", "no_description_rate", "no_role_rate", "nb_fossil_fuel_industry", "woman_proportion", "experience score", "nb_interventions"}) by_meeting = intervention_data.groupby("meeting") affiliation_data_by_meeting = affiliation_data.groupby("meeting") # default to 0 affiliation_data["nb_interventions"] = 0 for label, affiliations in by_meeting: interventions_by_country = affiliations.groupby("country") affiliation_data_meeting = affiliation_data_by_meeting.get_group(label) for country, rest in interventions_by_country: if country in set(affiliation_data["country"]): sum = rest["nb_interventions"].sum() affiliation_data.loc[(affiliation_data["meeting"] == label) & (affiliation_data["country"] == country), "nb_interventions"] = sum if sum > 0 else 0 entire_dataset = affiliation_data entire_dataset.to_csv("../data/data_regression/dataset_interventions.csv", encoding="utf-8-sig", index=False) -# prepare_intervention_data() +prepare_intervention_data() prepare_affiliation_data() fuse_datasets() diff --git a/report/predictive_modelling.tex b/report/predictive_modelling.tex index 9f893c7..c03742e 100644 --- a/report/predictive_modelling.tex +++ b/report/predictive_modelling.tex @@ -1,82 +1,107 @@ \section{Predictive Modelling} \label{predictive_modelling} Having extracted and processed the data contained in the participant lists, we use them to build predictive models for other data. First, we build linear models for the data on interventions at UNFCCC meetings collected by Tatiana Cogne and Victor Kristof (see \ref{tatiana}). Note that we can't go further on this topic due to time constraints, but there is more potential for creating models with our data, especially for the interaction dataset also collected by Tatiana Cogne and Victor Kristof. \subsection{Predict Interventions} The data on interventions lists for different UNFCCC meetings how many times a party intervenes in this meeting. We build a model that predicts for a party and a given meeting the number of interventions of the party at this meeting. Figure \ref{fig:interv_distr} plots the distribution of the interventions, i.e. the distribution of the labels of the complete dataset. Most parties don't have any intervention or only one, while some parties intervene a lot more. \begin{figure}[ht] \caption{Distribution of the intervention labels} \centering \includegraphics[width=0.7\textwidth]{distr_interventions.png} \label{fig:interv_distr} \end{figure} \subsubsection{Data samples} We define a data sample $x_i$ as the participation of a party at a meeting. Note that we only consider parties and no other affiliations as only parties are able to make interventions in the official negotiations. We thus define the structure of a data sample. \begin{equation*} x_i = \begin{bmatrix} year - 1995 \\ number\_of\_delegates \\ meeting\_type \\ government\_rate \\ diplomacy\_rate \\ security\_rate \\ press\_rate \\ university\_rate \\ no\_description\_rate \\ no\_keyword\_rate \\ nb\_fossil\_fuel\_industry\_associations \\ woman\_proportion \\ experience\_score\_cop \\ experience\_score\_sb \\ experience\_score\_parties\_rate \\ is\_Afghanistan \\ is\_Albania \\ \vdots \\ is\_Zimbabwe \\ is\_unrecognized\_country \end{bmatrix} \in \mathbb{R}^{214} \end{equation*} The attribute \textit{year} is the year the meeting took place and is substracted 1995 which is the year of the first meeting (SB1) to get values closer to zero. The attribute \textit{meeting\_type} is binary and 0 if the meeting was a COP, 1 if the meeting was a SB. The attributes \textit{government\_rate} to \textit{no\_keyword\_rate} correspond to the proportion of each role that we assign (see \ref{roles}). For the experience score, we provide COP and SB experience in total numbers, they sum up to the total experience score of an affiliation. The \textit{experience\_score\_parties\_rate} denotes the rate of the total experience score that has been acquired in parties (see \ref{experience}). The information about the parties are converted into 198 binary attributes, one for each of the 197 Parties to the Convention and one for an invalid or unrecognized country. \\ -In total, we have 9217 data samples. We randomly pick about 80\% of these samples, i.e. 7400 samples, as our training set. +In total, we have 9218 data samples. We randomly pick about 80\% of these samples, i.e. 7400 samples, as our training set. The resting samples form our test set. \subsubsection{Models} % baseline models We first build two \textbf{baseline models}, such that we are later able to compare our models to those simple models. The first baseline model consists simply of always predicting zero interventions, as this is the most common label. The second baseline model consists of computing the average number of interventions a party did over all included meetings and always predict this average. % linear model % linear model with logarithmic transformation % mixed model \subsubsection{Results} -To be able to quantify the results of our models, we first \ No newline at end of file +We will compare our models by the root-mean-square error (RMSE) between the predicted number of interventions $\hat{y_i}$ and the true values $y_i$. +The root-mean-square error is defined as the root of the mean squared error (MSE), i.e. for $n$ samples, +\begin{equation} + RMSE = \sqrt{MSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\hat{y_i} - y_i)^2} +\end{equation} +First, we consider the baseline models. When always predicting zero interventions, the test data yields $ RMSE = 9.54 $. +When we always predict the average number of interventions of the party in question during all samples in the training data, +the test data yields $ RMSE = 5.02 $. This shows that the information of the party already gives a lot of information about the behavior +during meetings. \\ +The ridge regression model with all attributes yields an $ RMSE = 4.82 $. % TODO wrong numbers +The optimal solution was found with cross-validation at regularizer $\lambda = 0.0155 $. +% TODO insert the same notation W +We can analyze attributes with the strongest influence on the prediction. +For this reason, we normalize the attributes before training the model. For an attribute $x_i,j$ we compute +\begin{equation} + x_i,j ' = \frac{x_{i,j} - \mu}{|x_{i,j}|} +\end{equation} % TODO put it to the model part. +The bias of the whole dataset is at $ w_0 = 3.246 $. The attributes with the strongest influence on the predictions are parties, as we expect seeing that the second +baseline model works pretty good. The highest tendency to many interventions per meetings is showed by the European Union ($ + 15.8 $), United States ($ + 53.8 $) and China ($ + 48.4 $). +Cote d'Ivoire ($ - 2.55 $), San Marino ($ - 2.54 $) and Greece ($ - 2.46 $) are the parties that bias the most towards little interventions. +When considering only non-party attributes, the top of the list towards more interventions are \textit{press\_rate} ($ + 2.51 $), \textit{university\_rate} ($ + 1.11 $) +and \textit{experience\_score\_parties\_rate} ($ + 0.70 $). +The non-party attributes that are lowering the predicted number of interventions the most are \textit{no\_description\_rate} ($ - 1.69 $), \textit{diplomacy_rate} ($ - 0.82 $) +and \textit{no_keyword_rate} ($ -0.44 $). +Interestingly, the year and the number of delegates are the attributes with the weakest influence on the prediction. Apparently, time and delegation size do +have a rather small influence on the activity of a party. diff --git a/report/report.aux b/report/report.aux index fb64dde..f02b965 100644 --- a/report/report.aux +++ b/report/report.aux @@ -1,116 +1,103 @@ \relax \providecommand\hyper@newdestlabel[2]{} \providecommand\HyperFirstAtBeginDocument{\AtBeginDocument} \HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined \global\let\oldcontentsline\contentsline \gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}} \global\let\oldnewlabel\newlabel \gdef\newlabel#1#2{\newlabelxx{#1}#2} \gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}} \AtEndDocument{\ifx\hyper@anchor\@undefined \let\contentsline\oldcontentsline \let\newlabel\oldnewlabel \fi} \fi} \global\let\hyper@last\relax \gdef\HyperFirstAtBeginDocument#1{#1} \providecommand\HyField@AuxAddToFields[1]{} \providecommand\HyField@AuxAddToCoFields[2]{} \providecommand\@newglossary[4]{} \@newglossary{main}{glg}{gls}{glo} 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