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R13271/AMP_measurements/visu.py
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visu.py
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import matplotlib.pyplot as plt
from collections import Counter
import numpy as np
import seaborn as sns
import pandas as pd
def plot_predictions_vs_actual(y_true, y_pred, title):
plt.figure(figsize=(10, 6))
sns.scatterplot(x=y_true, y=y_pred)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title(title)
plt.show()
def plot_classifications_by_antibiotic_level_with_counts(docs):
antibiotics_levels = []
living_states = []
for doc in docs:
antibiotics_levels.append(doc['antibiotics_quantity'])
living_states.append(doc['living_state'])
data = pd.DataFrame({'antibiotics_quantity': antibiotics_levels, 'living_state': living_states})
# Create a count plot
plt.figure(figsize=(14, 8))
sns.countplot(x='antibiotics_quantity', hue='living_state', data=data, palette='viridis')
plt.xlabel('Antibiotics Quantity')
plt.ylabel('Count')
plt.title('Model Classifications by Antibiotics Levels')
plt.legend(title='Living State', loc='upper right', labels=['Alive (0)', 'Dead (1)'])
plt.show()
def plot_classifications_by_antibiotic_level(docs, model, feature_set):
antibiotics_levels = []
living_states = []
for doc in docs:
antibiotics_levels.append(doc['antibiotics_quantity'])
living_states.append(doc['living_state'])
# Create a scatter plot
plt.figure(figsize=(12, 8))
plt.scatter(antibiotics_levels, living_states, c=living_states, cmap='viridis', marker='o', edgecolor='k')
plt.xlabel('Antibiotics Quantity')
plt.ylabel('Living State')
plt.title('Model Classifications by Antibiotics Levels')
plt.colorbar(label='Living State')
plt.xticks(list(set(antibiotics_levels)))
plt.show()
def plot_feature_correlations(features):
plt.figure(figsize=(20, 16)) # Increase figure size for better readability
correlation_matrix = features.corr()
sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap="coolwarm", annot_kws={"size": 8}, cbar_kws={"shrink": 0.75})
plt.title("Feature Correlation Matrix", fontsize=20)
plt.xticks(rotation=90, fontsize=10)
plt.yticks(fontsize=10)
plt.tight_layout() # Adjust layout to prevent clipping of tick-labels
plt.savefig('feature_corr.png') # Save plot to file
plt.close()
def plot_feature_importances(model, features):
importances = model.feature_importances_
indices = np.argsort(importances)[::-1]
feature_names = features.columns
plt.figure(figsize=(14, 8))
plt.title("Feature Importances")
plt.bar(range(len(importances)), importances[indices], align="center")
plt.xticks(range(len(importances)), feature_names[indices], rotation=90)
plt.tight_layout()
plt.savefig('feature_imp.png') # Save plot to file
plt.close()
def display_counts(docs, label):
antibiotics_counts = Counter(doc['antibiotics_quantity'] for doc in docs)
print(f"\n{label} Set Antibiotics Counts:")
for qty, count in antibiotics_counts.items():
print(f"{qty}: {count}")
def display_data(data):
mic_counter = Counter(doc['antibiotics_quantity'] for doc in data)
print("\nantibiotics Counts:")
for mic, count in mic_counter.items():
print(f"{mic}: {count}")
def plot_normalized_data(normalized_doc):
plt.figure(figsize=(14, 8))
# Plot data for OFF state
if 'data_OFF' in normalized_doc:
x_off = range(normalized_doc['indices_OFF'][0],
normalized_doc['indices_OFF'][0] + len(normalized_doc['data_OFF']))
plt.plot(x_off, normalized_doc['data_OFF'], label='OFF', color='blue')
# Plot data for ON state
if 'data_ON' in normalized_doc:
x_on = range(normalized_doc['indices_ON'][0], normalized_doc['indices_ON'][0] + len(normalized_doc['data_ON']))
plt.plot(x_on, normalized_doc['data_ON'], label='ON', color='green')
# Plot data for Trapping state
if 'data_Trapping' in normalized_doc:
x_trap = range(normalized_doc['indices_Trapping'][0],
normalized_doc['indices_Trapping'][0] + len(normalized_doc['data_Trapping']))
plt.plot(x_trap, normalized_doc['data_Trapping'], label='Trapping', color='red')
# Plot data for on_to_trapping state
if 'data_on_to_trapping' in normalized_doc:
x_on_to_trapping = range(normalized_doc['indices_on_to_trapping'][0],
normalized_doc['indices_on_to_trapping'][0] + len(normalized_doc['data_on_to_trapping']))
plt.plot(x_on_to_trapping, normalized_doc['data_on_to_trapping'], label='on_to_trapping', color='purple')
# Adding labels and title
plt.xlabel('Indices')
plt.ylabel('Normalized Transmission')
plt.title('Normalized Transmission Data for Different States')
plt.legend()
plt.show()
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