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RandomForest.py
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RandomForest.py
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import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn import metrics
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.model_selection import permutation_test_score, StratifiedKFold
from scipy.spatial import distance_matrix
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from cheminformatics import calc_dist_matrix, calc_D_cutoff, calc_test_distances, binary_stats
from collections import Counter
#from matplotlib.pyplot import cm
#import matplotlib.pyplot as plt
#plt.switch_backend('agg')
import numpy as np
import io
import base64
import plotly.express as px
import math
def RandomForestClassifierTrain(df, positives, balance_data=False):
#X = dataset.iloc[:, 0:4].values
#y = dataset.iloc[:, 4].values
print(positives)
pos_length=len(positives)
target_length=df.shape[0]
cols=df.columns
df['Target'] = df[[cols[0], cols[1]]].apply(":".join, axis=1)
if pos_length>0:
df['outcome'] = [1 if x in positives else 0 for x in df['Target']]
features = [x for x in cols if "|" in x]# Separating out the features
X_train = df.loc[:, features].values# Standardizing the features
#X_train = StandardScaler().fit_transform(x)# Fit Transform features
#y_train = df.loc[:,['outcome']].values# Separating out the target
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
X_train = StandardScaler().fit_transform(X_train)# Fit Transform features(X_train)
y_train = df['outcome'].to_numpy()
print(y_train)
# X_test = StandardScaler().transform(X_test)# Transform features(X_test)
print('Training set shape: %s' % Counter(y_train))
# classifier = RandomForestClassifier(n_estimators=20, random_state=0)
# classifier.fit(X_train, y_train)
# y_pred = classifier.predict(X_test)
# print(confusion_matrix(y_test,y_pred))
# print(classification_report(y_test,y_pred))
# print(accuracy_score(y_test, y_pred))
######
##%%time
# Number of trees in random forest
n_estimators = [100]#, 250, 500, 750, 1000]
max_features = ['auto', 'sqrt']
criterion = ['gini', 'entropy']
if balance_data == True:
class_weight = [None]
else:
class_weight = [None,'balanced',
{0:.9, 1:.1}, {0:.8, 1:.2}, {0:.7, 1:.3}, {0:.6, 1:.4},
{0:.4, 1:.6}, {0:.3, 1:.7}, {0:.2, 1:.8}, {0:.1, 1:.9}]
random_state = [24]
# Create the random grid
param_grid = {'n_estimators': n_estimators,
'max_features': max_features,
'criterion': criterion,
'random_state': random_state,
'class_weight': class_weight}
# setup model building
rf = GridSearchCV(RandomForestClassifier(), param_grid, n_jobs=-1, cv=5, verbose=1)
rf.fit(X_train, y_train)
print()
print('Best params: %s' % rf.best_params_)
print('Score: %.2f' % rf.best_score_)
rf_best = RandomForestClassifier(**rf.best_params_, n_jobs=-1)
rf_best.fit(X_train, y_train)
# Applicability Domain
training_distances = calc_dist_matrix(X_train)
D_cutoff = calc_D_cutoff(training_distances)
# Params
cross_val = StratifiedKFold(n_splits=5)
index = []
pred = []
prob = []
ad = []
# 5-fold external loop
for train_index, test_index in cross_val.split(X_train, y_train):
fold_model = rf_best.fit(X_train[train_index], y_train[train_index])
fold_pred = rf_best.predict(X_train[test_index])
fold_prob = rf_best.predict_proba(X_train[test_index])
pred.append(fold_pred)
prob.append(fold_prob)
index.append(test_index)
# Estimate AD for each fold
fold_distances_train = calc_dist_matrix(X_train[train_index])
fold_D_cutoff = calc_D_cutoff(fold_distances_train)
fold_distances_test = calc_test_distances(X_train[test_index], X_train[train_index])
fold_cpd_ad = []
for i in np.arange(0, len(fold_distances_test), 1):
if fold_distances_test[i] < fold_D_cutoff:
fold_cpd_ad.append('Inside')
else:
fold_cpd_ad.append('Outside')
ad.append(fold_cpd_ad)
# Prepare results
fold_index = np.concatenate(index)
fold_pred = np.concatenate(pred)
print(fold_pred)
fold_prob = np.concatenate(prob)
fold_prob = np.amax(fold_prob, axis=1).round(2)
fold_ad = np.concatenate(ad)
print('ok1')
five_fold_dwpc = pd.DataFrame({'Prediction': fold_pred, 'Confidence': fold_prob, 'AD': fold_ad}, index=list(fold_index))
five_fold_dwpc.AD[five_fold_dwpc.AD == 'Outside'] = np.nan
five_fold_dwpc.AD[five_fold_dwpc.AD == 'Inside'] = five_fold_dwpc.Prediction
five_fold_dwpc.sort_index(inplace=True)
five_fold_dwpc['y_train'] = pd.DataFrame(y_train)
five_fold_ad = five_fold_dwpc.dropna().astype(int)
coverage_5f = len(five_fold_ad) / len(five_fold_dwpc)
# Stats
dwpc = pd.DataFrame(binary_stats(five_fold_dwpc['y_train'], five_fold_dwpc['Prediction']))
dwpc['Coverage'] = 1.0
# AD stats
dwpc_ad = five_fold_dwpc.dropna(subset=['AD']).astype(int)
coverage_dwpc_ad = len(dwpc_ad['AD']) / len(five_fold_dwpc['y_train'])
dwpc_ad = pd.DataFrame(binary_stats(dwpc_ad['y_train'], dwpc_ad['AD']))
dwpc_ad['Coverage'] = round(coverage_dwpc_ad, 2)
# Print stats
print('\033[1m' + '5-fold External Cross Validation Statistical Characteristics' + '\n' + '\033[0m')
dwpc_5f_stats = dwpc.append(dwpc_ad)
dwpc_5f_stats.set_index([['DWPC', 'DWPC AD']], drop=True, inplace=True)
print(dwpc_5f_stats)
permutations = 20
score, permutation_scores, pvalue = permutation_test_score(rf_best, X_train, y_train,
cv=5, scoring='balanced_accuracy',
n_permutations=permutations,
n_jobs=-1,
verbose=1,
random_state=24)
print('True score = ', score.round(2),
'\nY-randomization = ', np.mean(permutation_scores).round(2),
'\np-value = ', pvalue.round(4))
report = f"5-fold External Cross Validation Statistical Characteristics\n\n\
True score = {score.round(2)}, \n\
Y-randomization = {np.mean(permutation_scores).round(2)}, \n\
p-value = {pvalue.round(4)}"
# Export stats
#if len(dwpc_ext) > 0: # If testing on true external set.
# morgan_stats = pd.concat([morgan, morgan_ad, ext_set_stats, ext_set_stats_ad], axis=0)
# morgan_stats.set_index([['5-fold CV', '5-fold CV AD', 'Ext. Withheld Set', 'Ext. Withheld Set AD']], drop=True, inplace=True)
# morgan_stats
#else:
dwpc_stats = dwpc_5f_stats.copy()
dwpc_stats.drop('Kappa', axis=1, inplace=True)
# Transpose morgan_stats
dwpc_stats_t = dwpc_stats.T
dwpc_stats_t = dwpc_stats_t.reset_index()
dwpc_stats_t = dwpc_stats_t.rename(columns={'index': 'Stats'})
# Make plot
plt.style.use('seaborn-colorblind')
fig, ax1 = plt.subplots(figsize=(8,5), dpi=90)
dwpc_stats_t.plot(kind='bar', ax=ax1, width=0.8)
ax1.set_xticklabels(labels=dwpc_stats_t['Stats'].tolist(), fontsize=14, rotation=0)
ax1.axhline(y=.6, color='indianred', ls='dashed')
#ax1.axhline(y=.4, xmax=0.16, color='indianred', ls='dashed')
ax1.legend_.remove()
plt.title('Statistical Characteristics of DWPC Random Forest', fontsize=16)
ax1.set_yticks(np.arange(0, 1.1, 0.1))
ax1.tick_params(labelsize=12)
handles, labels = ax1.get_legend_handles_labels()
ax1.legend(handles, labels, fontsize=12,
loc='upper center', bbox_to_anchor=(0.5, -0.09), ncol=4)
fig.tight_layout()
buf = io.BytesIO() # in-memory files
plt.savefig(buf, bbox_inches='tight', transparent=False, format='png', dpi=300) # save to the above file object
plt.close()
data = base64.b64encode(buf.getbuffer()).decode("utf8") # encode to html elements
return "data:image/png;base64,{}".format(data),report