import pandas as pd
import json
if __name__ == "__main__":
#python decisiontree.py [input_file] [output.json] [--addTreeFeatures]
if len(sys.argv) >= 3:
add_features = False;
if len(sys.argv) == 4:
if sys.argv[3] == "--addTreeFeatures":
add_features = True;
df = pd.read_csv(sys.argv[1], sep=',', header=0)
X = df.iloc[:,:-1].as_matrix()
y = df.iloc[:,-1].as_matrix().astype('U')
param_grid = {'criterion':['gini', 'entropy'], 'splitter': ['best', 'random'],
'max_depth': [3,4,5,6,7,8,9,10,11,12,13,14,15], 'min_samples_split': [2,3,4,5],
'min_samples_leaf': [1,2,3,4,5,6]}
classifier = DecisionTreeClassifier()
report_map = compute_metrics_classifier(classifier=classifier, param_grid = param_grid,
X = X, y = y, cv_parameter_estimation = 3, add_features=add_features)
f = open(sys.argv[2], "w")
json.dump(report_map, f)
f.close()
if len(sys.argv) >= 3:
add_features = False
if len(sys.argv) == 4:
if sys.argv[3] == "--addTreeFeatures":
add_features = True
df = pd.read_csv(sys.argv[1], sep=',', header=0)
X = df.iloc[:, :-1].as_matrix()
y = df.iloc[:, -1].as_matrix().astype('U')
param_grid = {
'n_estimators': [5, 10, 15, 20, 25, 30, 40, 50],
'criterion': ['gini', 'entropy'],
'max_features': ['auto', 'sqrt', 'log2', None],
'max_depth': [10, 20, 30, 40, 50, 100, None]
}
classifier = RandomForestClassifier()
report_map = compute_metrics_classifier(classifier=classifier,
param_grid=param_grid,
X=X,
y=y,
cv_parameter_estimation=3,
add_features=add_features)
f = open(sys.argv[2], "w")
json.dump(report_map, f)
f.close()
"classifier":RandomForestClassifier(),
"classifier_name":"RandomForestClassifier"
},
{
"feature":"Features/Lower_res/features_all.csv",
"use_tree":True,
"classifier_name":"SVC",
"classifier":SVC()
},
]
for id_experiment in range(len(to_do)):
print "========== running experiment " +str(id_experiment) +" of "+str(len(to_do))
df = pd.read_csv(to_do[id_experiment]["feature"], sep=',', header=0)
to_do[id_experiment]["metric_tests"] = []
X = df.iloc[:,:-1].as_matrix()
y = df.iloc[:,-1].as_matrix().astype('U')
for id_run in range(n_runs):
print("id run: "+ str(id_run))
report_map = compute_metrics_classifier(classifier=to_do[id_experiment]["classifier"], param_grid = param_grids[to_do[id_experiment]["classifier_name"]],
X = X, y = y, cv_parameter_estimation = 3, add_features = to_do[id_experiment]["use_tree"])
to_do[id_experiment]["metric_tests"].append(report_map["metric_test"])
del to_do[id_experiment]["classifier"]
f = open("summary_plots_18_runs.json", "w")
json.dump(to_do, f)
f.close()