classification_binary(
light_clf.SGDClassifier(random_state=RANDOM_SEED)),
# Decision trees
regression(tree.DecisionTreeRegressor(**TREE_PARAMS)),
regression(tree.ExtraTreeRegressor(**TREE_PARAMS)),
classification(tree.DecisionTreeClassifier(**TREE_PARAMS)),
classification(tree.ExtraTreeClassifier(**TREE_PARAMS)),
classification_binary(tree.DecisionTreeClassifier(**TREE_PARAMS)),
classification_binary(tree.ExtraTreeClassifier(**TREE_PARAMS)),
# Random forest
regression(ensemble.ExtraTreesRegressor(**FOREST_PARAMS)),
regression(ensemble.RandomForestRegressor(**FOREST_PARAMS)),
classification(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)),
classification(ensemble.RandomForestClassifier(**FOREST_PARAMS)),
classification_binary(ensemble.ExtraTreesClassifier(**FOREST_PARAMS)),
classification_binary(
ensemble.RandomForestClassifier(**FOREST_PARAMS)),
],
# Following is the list of extra tests for languages/models which are
# not fully supported yet.
# <empty>
)
def test_e2e(estimator, executor_cls, model_trainer, is_fast, global_tmp_dir):
sys.setrecursionlimit(RECURSION_LIMIT)
X_test, y_pred_true, fitted_estimator = model_trainer(estimator)
executor = executor_cls(fitted_estimator)
color='r',
label='Seven Clusters')
plt.xlabel('Unsupervised method')
plt.ylabel('Silhouette score')
plt.title('Silhouette Score on Various Clustering Methods and Cluster Sizes')
plt.xticks(index + bar_width, ('GMM', 'Kmeans', 'HAC'))
plt.legend()
plt.savefig(sys.argv[2] + '/silhouette_barplot.png')
###END PLOT SNIPPET###
###SUPERVISED PORTION
from sklearn import ensemble
from sklearn import cross_validation
##For each of random forest, naive bayes, logistic regression, run 10 fold CV and get the average score (mean accuracy)
randfor = ensemble.RandomForestClassifier(100)
randforscores = cross_validation.cross_val_score(randfor,
PCA_data,
class_labels,
cv=10)
avgrandforscore = numpy.mean(randforscores)
from sklearn import naive_bayes
nbayes = naive_bayes.GaussianNB()
nbayesscores = cross_validation.cross_val_score(nbayes,
PCA_data,
class_labels,
cv=10)
avgnbayesscores = numpy.mean(nbayesscores)
:param classifier: Model Name
:param feature_vector_train: Training input data
:param label: Training output label
:param feature_vector_valid: Testing input data
:return: Accuracy score
"""
# fit the training dataset on the classifier
classifier.fit(feature_vector_train, label)
# predict the labels on validation dataset
predictions = classifier.predict(feature_vector_valid)
return metrics.accuracy_score(predictions, y_test)
accuracy = train_model(svm.SVC(kernel='linear'), xtrain_tfidf, y_train,
xvalid_tfidf)
print "SVM, WordLevel TF-IDF: Accuracy:", accuracy * 100
print("\n")
accuracy = train_model(svm.SVC(kernel='linear'), xtrain_count, y_train,
xvalid_count)
print "SVM, CountVector: Accuracy:", accuracy * 100
print("\n")
accuracy = train_model(ensemble.RandomForestClassifier(n_estimators=100),
xtrain_tfidf, y_train, xvalid_tfidf)
print "RF, Wordlevel TF-IDF: Accuracy:", accuracy * 100
print("\n")
accuracy = train_model(ensemble.RandomForestClassifier(n_estimators=100),
xtrain_count, y_train, xvalid_count)
print "RF, CountVector: Accuracy:", accuracy * 100
print("\n")
# -*- coding: utf-8 -*-
from sklearn import ensemble
MODELS = {
"randomforest":
ensemble.RandomForestClassifier(n_estimators=200, n_jobs=-1, verbose=2),
"extratrees":
ensemble.ExtraTreesClassifier(n_estimators=200, n_jobs=-1, verbose=2)
}
if __name__ == '__main__':
now = datetime.now()
unigrams = loadVocabulary()
unigrams_flag = 'unigrams'
print 'load train set'
is_train_set = 1
labels = loadLabels(is_train_set)
features = loadFeatures(labels, unigrams, is_train_set)
train_set = {'labels': labels, 'features': features}
print 'load test set'
is_train_set = 0
labels = loadLabels(is_train_set)
features = loadFeatures(labels, unigrams, is_train_set)
test_set = {'labels': labels, 'features': features}
model = ensemble.RandomForestClassifier(n_estimators=20, random_state=512)
model_name = 'random forest'
trainModel(unigrams_flag, train_set, test_set, model, model_name)
model = ensemble.RandomForestClassifier(n_estimators=50, random_state=512)
trainModel(unigrams_flag, train_set, test_set, model, model_name)
topK = 100
getTopFeatures(model, topK, unigrams)
print 'running time is', datetime.now() - now
def getModel(dataset, model):
if model == Models.RandomForest:
if dataset == "IMDB":
return ensemble.RandomForestClassifier(
criterion='gini',
max_depth=600,
max_features=0.8,
max_leaf_nodes=100,
min_impurity_decrease=0.0001,
n_estimators=50)
else:
return ensemble.RandomForestClassifier(
min_impurity_decrease=0.0001,
random_state=30,
criterion='gini',
ccp_alpha=0.0002,
max_depth=200,
max_features=0.4,
n_estimators=90)
elif model == Models.DecisionTree:
if dataset == "IMDB":
return tree.DecisionTreeClassifier(max_depth=600,
min_impurity_decrease=0.0001,
max_leaf_nodes=100,
max_features=0.8,
splitter="random",
ccp_alpha=0.00025,
criterion='gini')
else:
return tree.DecisionTreeClassifier(max_depth=450,
min_impurity_decrease=0.0001,
max_leaf_nodes=600,
random_state=30,
max_features=0.4,
criterion='gini',
splitter="best",
ccp_alpha=0.00055)
elif model == Models.AdaBoost:
if dataset == "IMDB":
return ensemble.AdaBoostClassifier(n_estimators=300,
learning_rate=0.7,
random_state=0)
else:
return ensemble.AdaBoostClassifier(n_estimators=125,
learning_rate=0.5,
random_state=0)
elif model == Models.KNN:
if dataset == "IMDB":
return neighbors.KNeighborsClassifier(n_neighbors=525,
weights='uniform',
p=2)
else:
return neighbors.KNeighborsClassifier(n_neighbors=600,
weights='uniform',
p=2)
elif model == Models.LogisticRegression:
if dataset == "IMDB":
return linear_model.LogisticRegression(C=1.0,
dual=False,
max_iter=1000,
penalty='l1',
solver='liblinear',
tol=0.1)
else:
return linear_model.LogisticRegression(C=1.0,
dual=False,
max_iter=100,
penalty='l2',
solver='saga',
tol=0.01)
elif model == Models.SVM:
if dataset == "IMDB":
return svm.LinearSVC(C=0.1,
dual=False,
loss='squared_hinge',
max_iter=1000,
penalty='l2',
tol=0.1)
else:
return svm.LinearSVC(C=1.0,
dual=True,
fit_intercept=True,
loss='squared_hinge',
max_iter=5000,
penalty='l2',
tol=0.01)
import pandas as pd
from sklearn import ensemble
if __name__ == "__main__":
loc_train = "fullTrainM4.csv"
#loc_test = "fullTestM4.csv"
loc_test = "fullTrainM4.csv"
#loc_submission = "kaggle.forest.submission.csv"
loc_submission = "trainScore.csv"
df_train = pd.read_csv(loc_train)
df_test = pd.read_csv(loc_test)
feature_cols = [
col for col in df_train.columns if col not in ['repeater', 'id']
]
X_train = df_train[feature_cols]
X_test = df_test[feature_cols]
y = df_train['repeater']
#test_ids = df_test['id']
test_ids = df_test['id']
print "running RF ..."
clf = ensemble.RandomForestClassifier(n_estimators=500, n_jobs=-1)
clf.fit(X_train, y)
print "scoring ..."
with open(loc_submission, "wb") as outfile:
outfile.write("id,repeatProbability\n")
for e, val in enumerate(list(clf.predict_proba(X_test))):
outfile.write("%s,%s\n" % (test_ids[e], val[1]))
from sklearn import ensemble
from sklearn.utils import shuffle
import numpy as np
from sklearn import cross_validation
#load data from file
data = np.loadtxt('acceldata.txt', delimiter=',')
DT = data.transpose()
#split data into arrays
X = np.array(DT[0:-1]).transpose()
Y = np.array(DT[-1]).transpose()
#create training and test sets, 70% in training 30% in testing set
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, Y, test_size=0.30, random_state=0)
#construct random forest with 10 trees, creating bootstrap samples
rf = ensemble.RandomForestClassifier(n_estimators=10,
random_state=0,
bootstrap=True)
### fit and score the model
rf.fit(X_train, y_train)
data_y,
test_size=0.3,
random_state=4)
print('----------- DTREE WITH GINI IMPURITY CRITERION ------------------')
dtree_gini_mod = tree.DecisionTreeClassifier(criterion='gini')
dtree_gini_mod.fit(x_train, y_train)
preds_gini = dtree_gini_mod.predict(x_test)
print_multiclass_classif_error_report(y_test, preds_gini)
n_est = [100]
depth = [None]
for n in n_est:
for dp in depth:
# Create model and fit.
mod = ensemble.RandomForestClassifier(n_estimators=n, max_depth=dp)
mod.fit(x_train, y_train)
# Make predictions - both class labels and predicted probabilities.
preds = mod.predict(x_test)
print('---------- EVALUATING MODEL: n_estimators = ' + str(n) +
', depth =' + str(dp) + ' -------------------')
# Look at results.
print_multiclass_classif_error_report(y_test, preds)
#END-------------- Fatality or Injury ----------------------
#START-------------SPEEED LIMIT -----------------------------
features2 = list(data)
features2.remove('SPEED_LIMIT')
data_x = data[features2]
bag = {
'bid': bid_cluster_classifier_bagging,
'ask': ask_cluster_classifier_bagging
}
with open('../run_models/clusterAndClassify_Bagging.model', 'wb') as output:
pickle.dump(bag, output, -1)
bid_cluster_classifier_rfc = multiclass.OneVsOneClassifier(
estimator=ensemble.RandomForestClassifier(n_estimators=30,
criterion='gini',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features='auto',
max_leaf_nodes=None,
min_impurity_split=1e-07,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None),
n_jobs=-1)
bid_cluster_classifier_rfc.fit(trainFeatures, all_bid_labels)
print "Bid accuracy with Random Forest: ", bid_cluster_classifier_rfc.score(
trainFeatures, all_bid_labels)
ask_cluster_classifier_rfc = multiclass.OneVsOneClassifier(
estimator=ensemble.RandomForestClassifier(n_estimators=30,
criterion='gini',
X_test = np.array(df.drop(['application_key'],1)) X_test = preprocessing.scale(X_test)# # if pca applied X_test = np.array(pd.DataFrame(data = pca.transform(X_test))) X_test = preprocessing.scale(X_test)# # different classifiers tested for the price pred dataset clf1 = svm.SVC() clf2 = ske.RandomForestClassifier(n_estimators=100) clf3 = neighbors.KNeighborsClassifier(n_neighbors=5) clf4 = MLPClassifier(solver='lbfgs',hidden_layer_sizes=(150, 100, 50, 10, 3)) clf5 = DecisionTreeClassifier(criterion = "entropy", random_state = 10,max_depth=100, min_samples_leaf=5) clf6 = AdaBoostClassifier(DecisionTreeClassifier(max_depth=100), n_estimators=600, learning_rate=1) clfs = [clf2 ] # train model i = 1 for clf in clfs: epochs = 10 for epoch in range(epochs): X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.20, random_state=42)
pca_df.plot(x=0, y=1, kind='scatter')
variance_df = pandas.DataFrame({
'variance': pca.explained_variance_,
'principal component': pca_df.columns.tolist()
})
# adding one to pricnipal componetns (since there is no 0th compeonet)
variance_df['principal component'] = variance_df['principal component'] + 1
variance_df.plot(x='principal component', y='variance')
# looks like variance stops getting explained after first two components
pca_df_small = pca_df.ix[:, 0:1]
# getting a cross val score of transformed data
rf = ensemble.RandomForestClassifier(n_estimators=500)
roc_scores_rf_pca = cross_val_score(rf,
pca_df_small,
response_series,
cv=10,
scoring='roc_auc')
print roc_scores_rf_pca.mean()
# 74% accuracy
roc_scores_rf = cross_val_score(rf,
explanatory_df,
response_series,
cv=10,
scoring='roc_auc')
print roc_scores_rf.mean()
# In[ ]:
from sklearn import linear_model
from sklearn import tree
from sklearn import neighbors
from sklearn import ensemble
from sklearn import svm
from sklearn import gaussian_process
from sklearn import naive_bayes
from sklearn import neural_network
from sklearn.model_selection import cross_val_score
clfs = {}
clfs['lr'] = {'clf': linear_model.LogisticRegression(), 'name':'LogisticRegression'}
clfs['rf'] = {'clf': ensemble.RandomForestClassifier(n_estimators=750, n_jobs=-1), 'name':'RandomForest'}
clfs['tr'] = {'clf': tree.DecisionTreeClassifier(), 'name':'DecisionTree'}
clfs['knn'] = {'clf': neighbors.KNeighborsClassifier(n_neighbors=4), 'name':'kNearestNeighbors'}
clfs['svc'] = {'clf': svm.SVC(kernel="linear"), 'name': 'SupportVectorClassifier'}
clfs['nusvc'] = {'clf': svm.NuSVC(), 'name': 'NuSVC'}
clfs['linearsvc'] = {'clf': svm.LinearSVC(), 'name': 'LinearSVC'}
clfs['SGD'] = {'clf': linear_model.SGDClassifier(), 'name': 'SGDClassifier'}
clfs['GPC'] = {'clf': gaussian_process.GaussianProcessClassifier(), 'name': 'GaussianProcess'}
clfs['nb'] = {'clf': naive_bayes.GaussianNB(), 'name':'GaussianNaiveBayes'}
clfs['bag'] = {'clf': ensemble.BaggingClassifier(neighbors.KNeighborsClassifier(), max_samples=0.5, max_features=0.5), 'name': "BaggingClassifier"}
clfs['gbc'] = {'clf': ensemble.GradientBoostingClassifier(), 'name': 'GradientBoostingClassifier'}
clfs['mlp'] = {'clf': neural_network.MLPClassifier(hidden_layer_sizes=(10,8,3), alpha=1e-5, solver='lbfgs'), 'name': 'MultilayerPerceptron'}
# In[ ]:
##grid_dt_search.fit(x_train, y_train)
##print(grid_dt_search.best_params_)
##tree_model.set_params(criterion="gini", max_depth=4)
##tree_model.fit(x_train, y_train)
##feature_importance = np.array(list(zip(data.columns.values, tree_model.feature_importances_)),
## dtype=[('feature', 'S10'), ('importance', 'float')])
##most_important = np.sort(feature_importance, order="importance")[::-1]
##for i in most_important[0:5]:
## print(i)
grid_para_forest = {
'criterion': ['gini', 'entropy'],
'max_depth': range(1, 31),
'n_estimators': range(10, 110, 10)
}
from sklearn import ensemble
forest_model = ensemble.RandomForestClassifier()
grid_rf_search = ms.GridSearchCV(forest_model, grid_para_forest, cv=3, n_jobs=1)
grid_rf_search.fit(x_train, y_train)
print(grid_rf_search.best_params_)
forest_model.set_params(criterion='gini', max_depth=3, n_estimators=80)
forest_model.fit(x_train, y_train)
print(forest_model.score(x_test, y_test))
feature_importance = np.array(list(zip(data.columns.values, forest_model.feature_importances_)),
dtype=[('feature', 'S10'), ('importance', 'float')])
most_important = np.sort(feature_importance, order="importance")[::-1]
for i in most_important[0:5]:
print(i)
time_stamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
log_files = path.join(LOGS_PATH, 'log_benchmark_time.txt')
logging.basicConfig(filename=log_files + str(time_stamp),
level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(message)s')
logging.debug('This is a log message.')
models = [
# #alpha=0.0001, average=False, class_weight=None, epsilon=0.1,
# eta0=0.0, fit_intercept=True, max_iter=1000, tol=None,l1_ratio=0.15,
# learning_rate='optimal', loss='hinge'
(LogisticRegression(C=2), {
'C': [1, 10, 100]
}, 'Logistic_reg_scale'),
(ensemble.RandomForestClassifier(n_estimators=100), {
"max_depth": [3, None],
"max_features": ['auto', 3, 10],
"min_samples_split": [2, 3, 10],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]
}, 'RandomForest'),
(ensemble.GradientBoostingClassifier(), {}, 'Gradient boosting')
#(LateFusion(model2=svm.SVC(kernel='linear', C=1, probability=True),standardscaler=preprocessing.Normalizer()), {}, 'late_fusion(GB,SVM) fusion5:5')
# (LateFusion(model2=LogisticRegression(C=2, class_weight={0:0.1, 1:0.9})),{},'late_fusion(GB,LR(0.1,0.9)'),
# (LateFusion(model2=ensemble.GradientBoostingClassifier()),{},'late_fusion(GB, GB')
]
def run_nested_cv_fold(data_type='benchmark', n_split=5, cv_fold=5):
"""
def test_classification_tasks():
# ------------------------
# Load latest feature sets
# ------------------------
latest_feature_set_time = time.strftime('0')
for index, file in enumerate(os.listdir(OUT_PATH)):
if file.startswith("comb_dataset_") and file.endswith(".csv"):
file_name = os.path.splitext(file)[0].split('_')
timestamp = file_name[2] + '_' + file_name[3]
if timestamp > latest_feature_set_time:
latest_feature_set_time = timestamp
print(latest_feature_set_time)
features_path = OUT_PATH + 'comb_dataset_' + latest_feature_set_time + ".csv"
df = pd.read_csv(features_path, sep=',')
# df = pd.read_csv(OUT_PATH + 'dataset_20190513_115505.csv', sep=',')
# feature_set = ['structural', 'temporal', 'social']
# label_set = ['true', 'false', 'unverified', 'non-rumor']
# print(df.dtypes)
df = df.fillna(0)
# ----------------
# DROP COLUMNS
# ----------------
# if 'temporal___longest_length' in df:
# df = df.drop(columns=['temporal___longest_length'])
# if 'temp_longest_length' in df:
# df = df.drop(columns=['temp_longest_length'])
# ---------------------
# DROP ROWS (label)
# ---------------------
# df = df[df.label != 'unverified']
# df = df[df.label != 'non-rumor']
X = df.drop(columns=['tweet_id', 'label'])
y = df['label']
# print(df.shape, df['label'].value_counts().to_dict())
# print(df.info())
classifiers = {'RF': ensemble.RandomForestClassifier(),
'XGB': XGBClassifier(),
'ADAB': ensemble.AdaBoostClassifier(),
'GRADB': ensemble.GradientBoostingClassifier()}
for classifier_name in list(classifiers.keys()):
accuracy_results = []
f1_macro_results = []
f1_micro_results = []
max_accuracy = 0
for i in range(100):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20) # 5-fold cross validation
clf = classifiers[classifier_name]
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test) # predictions
accuracy = metrics.accuracy_score(y_test, y_pred)
f1_macro = f1_score(y_test, y_pred, average='macro')
f1_micro = f1_score(y_test, y_pred, average='micro')
# print("#{}: Accuracy={} F1-macro={} F1-micro={}".format(i, accuracy, round(f1_macro, 4), round(f1_micro, 4)))
accuracy_results.append(accuracy)
f1_macro_results.append(f1_macro)
f1_micro_results.append(f1_micro)
if accuracy > max_accuracy:
max_accuracy = accuracy
feature_importances = pd.DataFrame(clf.feature_importances_, index=X_train.columns,
columns=['importance']).sort_values('importance', ascending=False)
print('\n\n' + classifier_name)
print('MEAN \t STD\t MEDIAN')
print('ACC \t', round(st.mean(accuracy_results), 4), '+-', round(st.pstdev(accuracy_results), 4))
print('F1-macro\t', round(st.mean(f1_macro_results), 4), '+-', round(st.pstdev(f1_macro_results), 4))
print('F1-micro\t', round(st.mean(f1_micro_results), 4), '+-', round(st.pstdev(f1_micro_results), 4))
print(max_accuracy)
print(feature_importances)
print("==========================="*3)
X_train,X_test,Y_train,Y_test= train_test_split(train,a,test_size=0.3)
print("START TRAINNING")
#KNN TRAINNING
knn = KNeighborsClassifier()
knn.fit(X_train, Y_train)
y_predict = knn.predict(X_test)
print(y_predict)
print("KNN accuracy:",accuracy_score(Y_test,y_predict))
print("KNN precision:",precision_score(Y_test,y_predict, average=None))
# 建立 random forest 模型
forest = ensemble.RandomForestClassifier(n_estimators = 40)
forest_fit = forest.fit(X_train, Y_train)
# 預測
test_y_predicted = forest.predict(X_test)
print(test_y_predicted)
print("RF(40) accuracy:",accuracy_score(Y_test,test_y_predicted))
print("RF(40) precision:",precision_score(Y_test,test_y_predicted, average=None))
for i in range(len(Y_test)):
if Y_test[i]!=test_y_predicted[i]:
print(i,Y_test[i],test_y_predicted[i])
print("----------------------------------------------------------------------------------")
print("TESTING")
y_train = y_train.reset_index(drop=True)
x_test = x_test.reset_index(drop=True)
y_test = y_test.reset_index(drop=True)
'''IMPUTE MISSING VALUES FOR TRAIN AND TEST SEPERATELY'''
x_train = missingValueImpute(x_train)
x_test = missingValueImpute(x_test)
'''CLASSIFICATION OF THE PRICE BINS'''
#Most of the time people have an idea about the price range in which their rental will fail.
#For users who have no idea about the price range, we can first classify in which price bucket their rental can fall in
#and then do the bucket specific regression
#RandomForest Classifier to predict the price bins
randomForestClassifier = ensemble.RandomForestClassifier(
n_estimators=200,
max_features='auto',
max_depth=15,
min_samples_leaf=7,
random_state=25,
class_weight='balanced')
randomForestClassifier.fit(scale(x_train), y_train['price_bins'])
print(randomForestClassifier.score(scale(x_train), y_train['price_bins']))
print(randomForestClassifier.score(scale(x_test), y_test['price_bins']))
y_pred = randomForestClassifier.predict(scale(x_test))
report = metrics.classification_report(y_test['price_bins'], y_pred)
print(report)
#Logistic Regression
logistic = linear_model.LogisticRegression(random_state=23,
class_weight='balanced')
logistic.fit(scale(x_train), y_train['price_bins'])
print(logistic.score(scale(x_train), y_train['price_bins']))
kaggle_format,
delimiter=",",
fmt='%d,%d',
header='Id,Category',
comments='')
############# BUILT-IN FUNCTION #############
scoreBuffer = []
print 50 * '='
print "CROSS VALIDATION USING SCIKIT-LEARN"
print 50 * '='
for depth in depths:
print "DEPTH:", depth
clf = ensemble.RandomForestClassifier(n_estimators=5,
criterion='entropy',
max_depth=depth)
scores = computeCV_Score(clf, crossValidation_Data,
crossValidation_Labels, k)
scoreBuffer.append((scores).mean())
print "Depth:", depth, "Accuracy: %0.2f%% (+/- %0.2f)" % (
(scores).mean(), np.array(scores).std() / 2)
print 50 * '-'
maxScore = np.max(scoreBuffer)
maxScore_Index = scoreBuffer.index(maxScore)
print "Best Depth Value:", depths[
maxScore_Index], "Accuracy for that Depth:", np.around(maxScore, 3)
print 50 * '-'
print 20 * "*", "The End", 20 * "*"
np_features = df.as_matrix()
np_keys = keys.as_matrix()
features_train, features_test, keys_train, keys_test = train_test_split(np_features, np_keys, test_size=0.33, random_state=42)
print "type(features_train)=", type(features_train), "features_train.shape=", features_train.shape
print "type(features_test)=", type(features_test), "features_test.shape=", features_test.shape
print "type(keys_train)=", type(keys_train), "keys_train.shape=", keys_train.shape
print "type(keys_test)=", type(keys_test), "keys_test.shape=", keys_test.shape
del df
del np_features
del np_keys
start_time = time.time()
clf = ensemble.RandomForestClassifier(n_estimators=200,n_jobs=-1,random_state=0)
print("--- time to execute ensemble.RandomForestClassifier %s seconds ---" % (time.time() - start_time))
#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
#n_jobs : integer, optional (default=1)
# The number of jobs to run in parallel for both fit and predict.
# If -1, then the number of jobs is set to the number of cores.
# my I7-4790 has 4 cores.
print "fitting clf.fit"
start_time = time.time()
clf.fit(features_train, keys_train)
print("--- time to execute clf.fit %s seconds ---" % (time.time() - start_time))
print "predicting"
start_time = time.time()
keys_test_predicted = clf.predict(features_test)
pca = doPCA(x_train)
x_train = pca.transform(x_train)
x_test = pca.transform(x_test)
'''
clf = ensemble.ExtraTreesClassifier(n_estimators=800, min_samples_leaf=5)
clf = clf.fit(x_train, y_train)
model = feature_selection.SelectFromModel(clf, prefit=True)
x_train = model.transform(x_train)
x_test = model.transform(x_test)
print(np.shape(x_train))
# Train random forest classifier with gini-impurity
print("Begin training random forest with gini...")
clf = ensemble.RandomForestClassifier(n_estimators=800, min_samples_leaf=5)
clf.fit(x_train, y_train)
trng_acc = clf.score(x_train, y_train)
val_acc = clf.score(x_test, y_test)
rf1_pred = clf.predict(x_test)
print("Training accuracy: %f" %trng_acc)
print("Validation accuracy: %f" %val_acc)
joblib.dump(clf, "rf_gini.pkl")
# Train random forest classifier with entropy-impurity
print("Begin training random forest with entropy...")
clf = ensemble.RandomForestClassifier(n_estimators=800, min_samples_leaf=5, criterion='entropy')
clf.fit(x_train, y_train)
trng_acc = clf.score(x_train, y_train)
val_acc = clf.score(x_test, y_test)
rf2_pred = clf.predict(x_test)
markersClassifier = None
if learnAlgo == 'LogisticRegression':
#markersClassifier = linear_model.LogisticRegression(C=nbMarkers, penalty='l1', class_weight=classWeights)
markersClassifier = linear_model.LogisticRegression(max_iter=10000)
markerFeaturesSet = markerFeaturesSet.tocsr()
elif learnAlgo == 'SVM':
markersClassifier = svm.SVC(probability=True,
C=nbMarkers,
class_weight=classWeights)
markerFeaturesSet = markerFeaturesSet.tocsr()
elif learnAlgo == 'DecisionTreeClassifier':
markersClassifier = tree.DecisionTreeClassifier(min_samples_split=10,
min_density=1)
markerFeaturesSet = markerFeaturesSet.toarray()
elif learnAlgo == 'RandomForestClassifier':
markersClassifier = ensemble.RandomForestClassifier(
min_samples_split=10, min_density=1)
markerFeaturesSet = markerFeaturesSet.toarray()
elif learnAlgo == 'ExtraTreesClassifier':
markersClassifier = ensemble.ExtraTreesClassifier(min_samples_split=10,
min_density=1)
markerFeaturesSet = markerFeaturesSet.toarray()
print(' - fit dataset')
markersClassifier.fit(markerFeaturesSet, markerTargetsSet)
print(' - save model to file')
joblib.dump(markersClassifier, corpusModel + '/model_markers.txt')
# Compute permutation cost as edit distance adapted to sequences
def getSequenceDistance(s1, s2):
s1Len = len(s1)
s2Len = len(s2)
#Positive and Negative state of reviews
#+ve is 1, -ve is 2
for f in range(len(state)):
if state[f] == "positive":
state[f] = 1
elif state[f] == "negative":
state[f] = 2
#Splitting data into training and testing datasets
train_x, test_x, train_y, test_y = train_test_split(termdoc,
state,
test_size=0.3)
#Using Random Forest Classification Algorithm
rfc = ensemble.RandomForestClassifier()
rfc_scores = cross_val_score(rfc, termdoc, state, cv=10)
print 'Random forest mean accuracy : %.2f' % (rfc_scores.mean())
print 'Random forest std : %.2f' % (rfc_scores.std())
rfc.fit(train_x, train_y)
predict_train_y = rfc.predict(train_x)
predict_test_y = rfc.predict(test_x)
print confusion_matrix(test_y, predict_test_y)
print 'Precision score is : %.2f' % (precision_score(test_y, predict_test_y))
print 'Recall Score is : %.2f' % (recall_score(test_y, predict_test_y))
print 'F score is : %.2f' % (f1_score(test_y, predict_test_y))
#Predicting the review states
predicted_all = rfc.predict(termdoc)
def create_model(self, trial):
rf_max_depth = trial.suggest_int("rf_max_depth", 2, 32, log=True)
model = ensemble.RandomForestClassifier(
max_depth=rf_max_depth, n_estimators=10
)
return model
with open('../saves/exp_test_trim_1.pickle', 'rb') as f:
test_df = pickle.load(f)
train_data = train_df
test_data = test_df
scaler = MinMaxScaler()
scaler.partial_fit(train_data[cols])
scaler.partial_fit(test_data[cols])
train_input = scaler.transform(train_data[cols])
test_input = scaler.transform(test_data[cols])
param_grid = {
'n_estimators': [100, 200, 300, 400, 500],
'max_features': [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
}
# cv_generator = GroupKFold(n_splits=3)
for i in range(50):
CV_rfc = GridSearchCV(ensemble.RandomForestClassifier(),
param_grid=param_grid,
cv=10)
CV_rfc.fit(train_input, train_data['true_class'])
save_string = "../saves/cv_forest_model_aws" + str(i) + ".pickle"
with open(save_string, 'wb') as f:
pickle.dump(CV_rfc, f)
print(CV_rfc.best_params_)
print('finished')
import time
import pickle
filename = "ApneaData.pkl"
testPercent=20
features = []
classes = []
t = time.time()
f = open(filename,'rb')
data = pickle.load(f)
f.close()
np.random.shuffle(data)
for row in data:
features.append(row[:-1])
classes.append(row[-1])
inputLength = len(features)
testLength = int(inputLength*0.2)
train_features, train_classes=features[:-testLength], classes[:-testLength]
test_features,test_classes = features[-testLength:],classes[-testLength:]
print("preprocessing time:",(time.time()-t))
t=time.time()
clf=ensemble.RandomForestClassifier(n_estimators=30)
clf.fit(train_features,train_classes)
print("fitting time:",(time.time()-t))
t=time.time()
pred_classes=[]
for e in test_features:
pred_classes.append(clf.predict([e])[0])
score = accuracy_score(pred_classes,test_classes)*100
print("predicting time:",(time.time()-t))
print("Accuracy:",score)
from sklearn import ensemble, feature_extraction, preprocessing
# import data
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sampleSubmission.csv')
# drop ids and get labels
labels = train.target.values
train = train.drop('id', axis=1)
train = train.drop('target', axis=1)
test = test.drop('id', axis=1)
# encode labels
lbl_enc = preprocessing.LabelEncoder()
labels = lbl_enc.fit_transform(labels)
# train a random forest classifier
clf = ensemble.RandomForestClassifier(n_jobs=-1,
n_estimators=100,
max_features=50,
verbose=2)
clf.fit(train, labels)
# predict on test set
preds = clf.predict_proba(test)
# create submission file
preds = pd.DataFrame(preds, index=sample.id.values, columns=sample.columns[1:])
preds.to_csv('benchmark.csv', index_label='id')
from sklearn import tree
from sklearn import ensemble
models = {
"decision_tree_gini": tree.DecisionTreeClassifier(
criterion="gini"
),
"decision_tree_entropy": tree.DecisionTreeClassifier(
criterion='entropy'
),
"rf": ensemble.RandomForestClassifier(),
}
base_endrow = base_endrow + ppd
print(base_endrow)
test_startrow = base_endrow + 1
print(test_startrow)
test_endrow = test_startrow + ppd
print(test_endrow)
day = day + 1
print(day)
return(calendar)
# scramble one
one = one.sample(frac=1)
#print(system_data_stream(one, p, t))
rf_model = skens.RandomForestClassifier(n_estimators=10,oob_score=True, criterion='entropy')
calendar = []
base_startrow = 1
base_endrow = t
test_startrow = t + 1
test_endrow = test_startrow + p
day = 1
print(base_startrow)
print(base_endrow)
print(test_startrow)
print(test_endrow)
print(day)
def train_model_random_forest(train, labels):
# train a random forest classifier
model = ensemble.RandomForestClassifier(n_jobs=-1, n_estimators=1000)
model.fit(train, labels)
joblib.dump(model, 'rf_model2.model')
return model