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solarPOT/ML_tutorial/lib_file.py
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lib_file.py
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import numpy as np
import pandas as pd
from matplotlib.patches import Patch
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.neighbors import KNeighborsRegressor # The KNN regression model
from sklearn.model_selection import cross_val_score # The cross-validation function
from sklearn.preprocessing import scale
def plot_cv_indices(cv, X, y, ax, n_splits, lw=10, cmap_cv = 'coolwarm'):
"""Create a sample plot for indices of a cross-validation object."""
"""Adapted from: https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html"""
# Get colormap
cmap_cv = plt.cm.get_cmap( cmap_cv )
# Generate the training/testing visualizations for each CV split
for ii, (tr, tt) in enumerate(cv.split(X=X, y=y)):
# Fill in indices with the training/test groups
indices = np.array([np.nan] * len(X))
indices[tt] = 1
indices[tr] = 0
# Visualize the results
ax.scatter(range(len(indices)), [ii + .5] * len(indices),
c=indices, marker='_', lw=lw, cmap=cmap_cv,
vmin=-.2, vmax=1.2)
# Formatting
yticklabels = list(range(n_splits))
ax.set(yticks=np.arange(n_splits) + .5, yticklabels=yticklabels,
xlabel='Sample index', ylabel="CV iteration",
ylim=[n_splits+.2, -.2], xlim=[0, len(X)])
ax.set_title('{}'.format(type(cv).__name__), fontsize=15)
ax.legend([Patch(color=cmap_cv(.8)), Patch(color=cmap_cv(.02))],
['Validation set', 'Training set'], loc=(1.02, .8))
return ax
def format_df(df, cmap, decimals = 3, vmin = None, vmax = None):
if vmin is None: vmin = df.min().min()
if vmax is None: vmax = df.max().max()
return df.style.background_gradient(cmap=cmap, vmin = vmin, vmax = vmax).format('{:.%d}' %decimals) # Formatting
def map_radiation_class(Gt):
cls = pd.Series(index = Gt.index, dtype = 'object')
cls[:] = 'Very good'
cls[Gt < 1400] = 'Good'
cls[Gt < 1200] = 'Average'
cls[Gt < 1000] = 'Bad'
cls[Gt < 800 ] = 'Very bad'
return cls
def cross_val_score_default_knn():
t = 'tilted_radiation'
f = ['roof_tilt', 'roof_aspect', 'roof_area', 'roof_x', 'roof_y', 'SIS',
'SISDIR', 'DHI', 'horizon_S', 'horizon_SSW', 'horizon_SWW', 'horizon_W',
'horizon_NWW', 'horizon_SSE', 'horizon_SEE', 'horizon_E', 'horizon_NEE',]
d = pd.read_csv( 'training_sample.csv' , index_col = 0 )
knn = KNeighborsRegressor() # Initiate model
return cross_val_score(knn, scale(d[f]), d[t], cv = 5)
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