def select_rfe(X, y, k_features=3):
'''
Signature: rfe(predictors, target, k_features=3)
Docstring:
Parameters
----------
pandas.core.frame.DataFrame
Returns
-------
'''
lm = LinearRegression()
rfe_init = rfe(lm, k_features)
rfe_init.fit(X, y)
rfe_mask = rfe_init.support_
rfe_features = X.iloc[:, rfe_mask].columns.to_list()
print(f"Recursive Feature Elimination: {len(rfe_features)} features")
print(rfe_features)
return None
#retain unscaled features
post2000_exp = pd.concat([numfeat_post, catfeat_post_bin],axis = 1)
post2000_exp = post2000_exp[pre2000_exp.columns]
#BUILD DECISION TREE MODEL (SCALED DATA)---------------------------------------
#instantiate decision tree model with coeff
dt = TreeClassifierWithCoef(criterion = 'gini', splitter = 'best', max_features = None,
max_depth = None, min_samples_split = 2, min_samples_leaf = 2,
max_leaf_nodes = None, random_state = 1)
#conduct recursive feature search
dt_rfe_cv = rfe(estimator=dt, step=1, cv=10, scoring='roc_auc', verbose = 1)
dt_rfe_cv.fit(pre2000_exp_scaled, pre2000_res)
#identify and plot optimal number of features (d = 17), ROC_AUC = 0.8262
print dt_rfe_cv.n_features_
print dt_rfe_cv.grid_scores_.max()
plt.figure()
plt.xlabel("DT: Number of Features selected")
plt.ylabel("DT: Cross Validation Score (ROC_AUC)")
plt.plot(range(1, len(dt_rfe_cv.grid_scores_) + 1), dt_rfe_cv.grid_scores_)
plt.show()
#identify selected features
dt_features = pre2000_exp_scaled.columns[dt_rfe_cv.get_support()]
print dt_features
df_gini, df_pc, on="Features", how="inner"
) # Join by column while keeping only items that exist in both, select outer or left for other options
df_features = df_join["Features"] # Save features from data frame
features = df_features.tolist() # Convert to list
## Setup Predictors and RFE
df_rfe = df_nev[features] # Add selected features to df
df_rfe["outcome"] = df_nev["outcome"] # Add outcome to RFE df
df_rfe = df_rfe.dropna() # Drop all columns with NA
X = df_rfe[df_features] # Save features columns as predictor data frame
Y = df_rfe["outcome"] # Use outcome data frame
Log_RFE = LogisticRegression(
solver="liblinear",
max_iter=4000) # Use regression coefficient as estimator
selector = rfe(
estimator=Log_RFE, step=1, min_features_to_select=1
) # define selection parameters, in this case all features are selected. See Readme for more ifo
## Run Recursive Feature Selection
selected = selector.fit(X, Y) # This will take time
## Output RFE results
ar_rfe = selected.support_ # Save Boolean values as numpy array
l_rfe = list(zip(X, ar_rfe)) # Create list of variables alongside RFE value
df_rfe = pd.DataFrame(l_rfe, columns=[
"Features", "RFE"
]) # Create data frame of importances with variables and gini column names
df_rfe = df_rfe[df_rfe.RFE == True] # Select Variables that were True
df_rfe = df_rfe.drop(columns=["RFE"]) # Drop Unwanted Columns
## Verify
#scale data
scaler = pp.StandardScaler()
scaler.fit(post2000_exp)
post2000_exp_scaled = pd.DataFrame(scaler.transform(post2000_exp),
index=post2000_exp.index,
columns=post2000_exp.columns)
#retain unscaled features
post2000_exp = pd.concat([numfeat_post, catfeat_post_bin], axis=1)
post2000_exp = post2000_exp[pre2000_exp.columns]
#IDENTIFY POTENTIAL FEATURES WITH RECURSIVE FEATURE SEARCH AND 10-FOLD CV------
#run recursive feature search with 10-fold cv to identify potential features
lr = lm.LogisticRegression()
lr_rfe_cv = rfe(estimator=lr, step=1, cv=10, scoring='roc_auc', verbose=1)
lr_rfe_cv.fit(pre2000_exp_scaled, pre2000_res)
#identify features
features = pre2000_exp_scaled.columns[lr_rfe_cv.get_support()]
print features
#run 10-fold CV to get scores with selected features (ROC_AUC = 0.9451)
lr_cv = cv(lr,
pre2000_exp_scaled[features],
pre2000_res,
cv=10,
scoring='roc_auc')
lr_cv.mean()
#create dataset with response and selected features
# BUILD DECISION TREE MODEL (SCALED DATA)---------------------------------------
# instantiate decision tree model with coeff
dt = TreeClassifierWithCoef(
criterion="gini",
splitter="best",
max_features=None,
max_depth=None,
min_samples_split=2,
min_samples_leaf=2,
max_leaf_nodes=None,
random_state=1,
)
# conduct recursive feature search
dt_rfe_cv = rfe(estimator=dt, step=1, cv=10, scoring="roc_auc", verbose=1)
dt_rfe_cv.fit(pre2000_exp_scaled, pre2000_res)
# identify and plot optimal number of features (d = 17), ROC_AUC = 0.8262
print dt_rfe_cv.n_features_
print dt_rfe_cv.grid_scores_.max()
plt.figure()
plt.xlabel("DT: Number of Features selected")
plt.ylabel("DT: Cross Validation Score (ROC_AUC)")
plt.plot(range(1, len(dt_rfe_cv.grid_scores_) + 1), dt_rfe_cv.grid_scores_)
plt.show()
# identify selected features
dt_features = pre2000_exp_scaled.columns[dt_rfe_cv.get_support()]
print dt_features
scaler.fit(post2000_exp)
post2000_exp_scaled = pd.DataFrame(scaler.transform(post2000_exp),
index = post2000_exp.index,
columns = post2000_exp.columns)
#retain unscaled features
post2000_exp = pd.concat([numfeat_post, catfeat_post_bin],axis = 1)
post2000_exp = post2000_exp[pre2000_exp.columns]
#IDENTIFY POTENTIAL FEATURES WITH RECURSIVE FEATURE SEARCH AND 10-FOLD CV------
#run recursive feature search with 10-fold cv to identify potential features
lr = lm.LogisticRegression()
lr_rfe_cv = rfe(estimator=lr, step=1, cv=10, scoring='roc_auc', verbose = 1)
lr_rfe_cv.fit(pre2000_exp_scaled, pre2000_res)
#identify features
features = pre2000_exp_scaled.columns[lr_rfe_cv.get_support()]
print features
#run 10-fold CV to get scores with selected features (ROC_AUC = 0.9451)
lr_cv = cv(lr, pre2000_exp_scaled[features], pre2000_res, cv=10, scoring='roc_auc')
lr_cv.mean()
#create dataset with response and selected features
lrset = pd.concat([pre2000_exp_scaled[features], pre2000_res], axis=1)
axis=0)
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
proba = [0] * 5
for f in range(x_train.shape[1]):
#each line in proba correspond to one histone marker
print("%d. Histone %d (%f)" %
(f + 1, indices[f] % 5, importances[indices[f]]))
proba[indices[f] % 5] += importances[indices[f]]
print(proba)
print(sum(proba))
"""-> Features selection with RFECV with our logistic regression classifier as estimator"""
rfecv = rfe(estimator=XGBClassifier(n_estimators=650, max_depth=18),
step=50,
verbose=1,
cv=10)
rfecv.fit(x_train, y_train)
rfecv_scores = rfecv.grid_scores_
#now we are going to identify the number of features selected from each histone marker
support = rfecv.support_
nb_selected_features = 0
histone_marker = []
#histone marker H3K4me3
for i in range(0, 500, 5):
if (support[i] == True):
nb_selected_features += 1
histone_marker.append(['H3K4me3', nb_selected_features])
#histone marker H3K4me1
