def test_rf_classifier_decision_path_leaf(self):
model = RandomForestClassifier(n_estimators=3, max_depth=3)
X, y = make_classification(3, n_features=4, random_state=42)
X = X[:, :2]
model.fit(X, y)
initial_types = [('input', FloatTensorType((None, X.shape[1])))]
model_onnx = convert_sklearn(model,
initial_types=initial_types,
options={
id(model): {
'decision_leaf': True,
'decision_path': True,
'zipmap': False
}
},
target_opset=TARGET_OPSET)
sess = InferenceSession(model_onnx.SerializeToString())
res = sess.run(None, {'input': X.astype(numpy.float32)})
pred = model.predict(X)
assert_almost_equal(pred, res[0].ravel())
dec = model.decision_path(X)
exp_leaf = path_to_leaf(model.estimators_, dec[0].todense(), dec[1])
exp_path = binary_array_to_string(dec[0].todense())
got_path = numpy.array([''.join(row) for row in res[2]])
assert exp_path == got_path.ravel().tolist()
assert exp_leaf.tolist() == res[3].tolist()
def test_drf_classifier_backupsklearn(backend='auto'):
df = pd.read_csv("./open_data/creditcard.csv")
X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
import h2o4gpu
Solver = h2o4gpu.RandomForestClassifier
#Run h2o4gpu version of RandomForest Regression
drf = Solver(backend=backend, random_state=1234, oob_score=True)
print("h2o4gpu fit()")
drf.fit(X, y)
#Run Sklearn version of RandomForest Regression
from sklearn.ensemble import RandomForestClassifier
drf_sk = RandomForestClassifier(random_state=1234, oob_score=True, max_depth=3)
print("Scikit fit()")
drf_sk.fit(X, y)
if backend == "sklearn":
assert (drf.predict(X) == drf_sk.predict(X)).all() == True
assert (drf.predict_log_proba(X) == drf_sk.predict_log_proba(X)).all() == True
assert (drf.predict_proba(X) == drf_sk.predict_proba(X)).all() == True
assert (drf.score(X, y) == drf_sk.score(X, y)).all() == True
assert (drf.decision_path(X)[1] == drf_sk.decision_path(X)[1]).all() == True
assert (drf.apply(X) == drf_sk.apply(X)).all() == True
print("Estimators")
print(drf.estimators_)
print(drf_sk.estimators_)
print("n_features")
print(drf.n_features_)
print(drf_sk.n_features_)
assert drf.n_features_ == drf_sk.n_features_
print("n_classes_")
print(drf.n_classes_)
print(drf_sk.n_classes_)
assert drf.n_classes_ == drf_sk.n_classes_
print("n_features")
print(drf.classes_)
print(drf_sk.classes_)
assert (drf.classes_ == drf_sk.classes_).all() == True
print("n_outputs")
print(drf.n_outputs_)
print(drf_sk.n_outputs_)
assert drf.n_outputs_ == drf_sk.n_outputs_
print("Feature importance")
print(drf.feature_importances_)
print(drf_sk.feature_importances_)
assert (drf.feature_importances_ == drf_sk.feature_importances_).all() == True
print("oob_score")
print(drf.oob_score_)
print(drf_sk.oob_score_)
assert drf.oob_score_ == drf_sk.oob_score_
def generate_mapping(self, X, y):
X = self.dummy_encoder.transform(X.copy(deep=True))
y = y.copy(deep=True)
mapping = []
for switch in self.dummy_encoder.mapping:
col = switch.get('col')
values = switch.get('mapping').copy(deep=True)
if isinstance(self.max_depth, int):
max_depth = self.max_depth
elif isinstance(self.max_depth, float):
max_depth = round(self.max_depth * values.shape[1])
else:
max_depth = min(self.max_depth[1],
round(self.max_depth[0] * values.shape[1]))
if max_depth == 0:
continue
forest = RandomForestClassifier(
max_depth=max_depth,
n_estimators=self.n_estimators,
n_jobs=self.n_jobs,
)
forest.fit(X[values.columns], y)
subsets = self.get_subsets(forest.decision_path(values))
subset_df = pd.DataFrame(data=subsets,
index=values.index,
columns=[
'{col}_subset_{i}'.format(col=col,
i=i)
for i in range(subsets.shape[1])
])
base_df = values.join(subset_df)
mapping.append({'col': col, 'mapping': base_df})
return mapping
def test_randomforestclassifier_decision_path(self):
model = RandomForestClassifier(max_depth=2, n_estimators=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2].astype(numpy.float32)
model.fit(X, y)
model_onnx = to_onnx(
model,
X,
options={id(model): {
'decision_path': True,
'zipmap': False
}})
sess = OnnxInference(model_onnx)
res = sess.run({'X': X})
pred = model.predict(X)
self.assertEqualArray(pred, res['label'].ravel())
prob = model.predict_proba(X)
self.assertEqualArray(prob, res['probabilities'])
dec = model.decision_path(X)
exp = binary_array_to_string(dec[0].todense())
got = numpy.array([''.join(row) for row in res['decision_path']])
self.assertEqual(exp, got.tolist())
def test_randomforestclassifier_decision_path(self):
model = RandomForestClassifier(max_depth=2, n_estimators=2)
X, y = make_classification(10, n_features=4, random_state=42)
X = X[:, :2]
model.fit(X, y)
initial_types = [('input', FloatTensorType((None, X.shape[1])))]
model_onnx = convert_sklearn(
model,
initial_types=initial_types,
options={id(model): {
'decision_path': True,
'zipmap': False
}})
sess = InferenceSession(model_onnx.SerializeToString())
res = sess.run(None, {'input': X.astype(numpy.float32)})
pred = model.predict(X)
assert_almost_equal(pred, res[0].ravel())
prob = model.predict_proba(X)
assert_almost_equal(prob, res[1])
dec = model.decision_path(X)
exp = binary_array_to_string(dec[0].todense())
got = numpy.array([''.join(row) for row in res[2]])
assert exp == got.ravel().tolist()
n_outputs = clf.n_outputs_ # the number of outputs when the model is built
importance = clf.feature_importances_ # an array containing the fractional importance of each feature
oob_score = clf.oob_score_ # score the training dataset using an out-of-bag estimator, this computes the average of correct classifications
# basically the coefficent of determination of R**2 using 'unseen' data not used to build the model
oob_decision_func = clf.oob_decision_function_
# now looking at the methods
leaf_indicies = clf.apply(
x_test
) # Using apply - which says which end leaf each row in x_test ends in
# using decision_path -
indicator, n_nodes_ptr = clf.decision_path(x_test)
parameters = clf.get_params()
predicted_array = clf.predict(x_test) # running the test set through the model
log_mean_predicted_class = clf.predict_log_proba(x_test)
mean_predicted_class = clf.predict_proba(x_test)
mean_accuracy = clf.score(
x_test, y_test
) # returns the accuracy (coefficent of determination of R**2) of the predicted test data outputs and the true values of the test data
# calcuate the R^2 of the model on the training set
r_2_train = clf.score(x_train, y_train)
labels = folhas.pop('Class').values
images = folhas.values
rf = RandomForestClassifier(n_estimators=25)
fit = rf.fit(images, labels)
predict = cross_val_predict(rf, images, labels, cv=10)
score = cross_val_score(rf, images, labels, cv=10)
prob = rf.predict_proba(images)
print("\n\n\n\n\nLeaf Values:\n")
print(folhas.head(340))
print("\n\nProbability:\n")
print(prob)
print("\n\nCross validation score:\n")
print(score)
print("\n\nDecision path:\n")
print(rf.decision_path(images))
print("\n\nAccuracy:\n")
print(accuracy_score(labels, predict))
feat_importances = pd.Series(rf.feature_importances_, index=folhas.columns)
feat_importances.nlargest(14).plot(kind='barh',
title='Importancia dos Atributos/Features')
cm = confusion_matrix(labels, predict)
plt.matshow(cm)
plt.ylabel('X')
plt.xlabel('Y')
plt.title('MATRIZ DE CONFUSAO')
plt.colorbar()
plt.show()
def test_drf_classifier_backupsklearn(backend='auto'):
df = pd.read_csv("./open_data/creditcard.csv")
X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
import h2o4gpu
Solver = h2o4gpu.RandomForestClassifier
#Run h2o4gpu version of RandomForest Regression
drf = Solver(backend=backend, random_state=1234, oob_score=True)
print("h2o4gpu fit()")
drf.fit(X, y)
#Run Sklearn version of RandomForest Regression
from sklearn.ensemble import RandomForestClassifier
drf_sk = RandomForestClassifier(random_state=1234,
oob_score=True,
max_depth=3)
print("Scikit fit()")
drf_sk.fit(X, y)
if backend == "sklearn":
assert (drf.predict(X) == drf_sk.predict(X)).all() == True
assert (drf.predict_log_proba(X) == drf_sk.predict_log_proba(X)
).all() == True
assert (drf.predict_proba(X) == drf_sk.predict_proba(X)).all() == True
assert (drf.score(X, y) == drf_sk.score(X, y)).all() == True
assert (drf.decision_path(X)[1] == drf_sk.decision_path(X)[1]
).all() == True
assert (drf.apply(X) == drf_sk.apply(X)).all() == True
print("Estimators")
print(drf.estimators_)
print(drf_sk.estimators_)
print("n_features")
print(drf.n_features_)
print(drf_sk.n_features_)
assert drf.n_features_ == drf_sk.n_features_
print("n_classes_")
print(drf.n_classes_)
print(drf_sk.n_classes_)
assert drf.n_classes_ == drf_sk.n_classes_
print("n_features")
print(drf.classes_)
print(drf_sk.classes_)
assert (drf.classes_ == drf_sk.classes_).all() == True
print("n_outputs")
print(drf.n_outputs_)
print(drf_sk.n_outputs_)
assert drf.n_outputs_ == drf_sk.n_outputs_
print("Feature importance")
print(drf.feature_importances_)
print(drf_sk.feature_importances_)
assert (drf.feature_importances_ == drf_sk.feature_importances_
).all() == True
print("oob_score")
print(drf.oob_score_)
print(drf_sk.oob_score_)
assert drf.oob_score_ == drf_sk.oob_score_
],
]
with open('dataset/gcc_bash.pickle', 'rb') as f:
gcc_bash = pickle.load(f)
with open('dataset/clang_bash.pickle', 'rb') as f:
clang_bash = pickle.load(f)
with open('dataset/tcc_bash.pickle', 'rb') as f:
tcc_bash = pickle.load(f)
# grep_clang_tcc_features, grep_clang_tcc_labels, _ = prepare_pos(clang_grep, tcc_grep)
bash_gcc_clang_features, bash_gcc_clang_labels, bash_gcc_clang_names = prepare_pos(
gcc_bash, clang_bash)
bash_tcc_clang_features, bash_tcc_clang_labels, bash_tcc_clang_names = prepare_pos(
tcc_bash, clang_bash)
rfc = RandomForestClassifier(random_state=42)
train_features, train_labels = prepare_training_data(training_data_files)
rfc.fit(train_features, train_labels)
paths, _ = rfc.decision_path(bash_gcc_clang_features)
base, _ = rfc.decision_path([bash_tcc_clang_features[10]])
sims = [(cos_sim(path, base), i) for i, path in enumerate(paths)]
sims = sorted(sims, key=lambda x: x[0], reverse=True)
for sim, i in sims[:3]:
print(bash_gcc_clang_names[i])
print(sim)
for sim, i in sims[-3:]:
print(bash_gcc_clang_names[i])
print(sim)
print(bash_gcc_clang_names[sims[0][1]])
print(bash_tcc_clang_names[10])
clf = RandomForestClassifier(max_features='sqrt',
n_jobs=2,
random_state=RANDOM_STATE)
clf.fit(train_data, train_label)
print('Read testing data...')
with open('testing.csv', 'r') as reader:
test_data = []
for line in reader.readlines():
pixels = list(map(float, line.rstrip().split(',')))
test_data.append(pixels)
print('Loaded ' + str(len(test_data)))
print('Predicting...')
test_data = np.array(test_data)
decision_path = clf.decision_path(test_data)
feature_label = []
for i in range(3088):
feature_label.append(str(i))
# understand tree structure
# ref 1: http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html
# ref 2: https://stackoverflow.com/questions/40155128/plot-trees-for-a-random-forest-in-python-with-scikit-learn
# parse tree structure and save the info by txt, png, and dot
f = open('rf_tree_explanation.txt', 'a')
for treeIndex in range(len(clf.estimators_)):
f.write('Tree number %d\n' % (treeIndex + 1))
n_nodes = clf.estimators_[treeIndex].tree_.node_count
children_left = clf.estimators_[treeIndex].tree_.children_left
children_right = clf.estimators_[treeIndex].tree_.children_right
#data for both classification and regression
X_train = np.random.rand(n_samples, 10)
y_train = np.random.randint(num_classes, size=(n_samples))
if (predicttype == 'classify'):
forest = RandomForestClassifier(n_estimators=n_trees, oob_score=True)
else:
forest = RandomForestRegressor(n_estimators=n_trees, oob_score=True)
oob_indices, oob_leaves_id, OOB_tree_indicator = {}, {}, {}
#fit
forest.fit(X_train, y_train)
forest_oob_score = forest.oob_score_
n_trees, train_size = forest.n_estimators, len(y_train)
indicator, n_nodes_ptr = forest.decision_path(X_train)
node_indicator = {}
sample_index = {}
for t, estimator in enumerate(forest):
oob_indices[t] = _generate_unsampled_indices(estimator.random_state,
X_train.shape[0])
oob_leaves_id[t] = estimator.apply(X_train[oob_indices[t], :])
sample_index[t] = _generate_sample_indices(estimator.random_state,
n_samples)
node_indicator[t] = indicator[:, n_nodes_ptr[t]:n_nodes_ptr[t + 1]]
mean_vals = {}
for t in range(n_trees):
mean_vals[t] = np.zeros(node_indicator[t].shape[1])
for node in range(node_indicator[t].shape[1]):
r, c = node_indicator[t][:, node].nonzero()
mean_vals[t][node] = np.mean(y_train[sample_index[t]][r])
class WaveRandomForestClassifier(BaseEstimator, ClassifierMixin):
"""
RandomForest based classifier but with nodes that are removed
See Paper:
Wavelet decomposition of Random Forests
http://www.jmlr.org/papers/volume17/15-203/15-203.pdf
"""
def __init__(
self,
n_estimators=100,
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_decrease=0.0,
min_impurity_split=None,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
nodes_to_keep=0.9,
):
self.n_estimators = n_estimators
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.bootstrap = bootstrap
self.oob_score = oob_score
self.n_jobs = n_jobs
self.random_state = random_state
self.verbose = verbose
self.warm_start = warm_start
self.class_weight = class_weight
self.nodes_to_keep = nodes_to_keep
self.forest = None
def fit(self, X, y):
# 1) create RandomForest
self.forest = RandomForestClassifier(
n_estimators=self.n_estimators,
criterion=self.criterion,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
min_weight_fraction_leaf=self.min_weight_fraction_leaf,
max_features=self.max_features,
max_leaf_nodes=self.max_leaf_nodes,
min_impurity_decrease=self.min_impurity_decrease,
min_impurity_split=self.min_impurity_split,
bootstrap=self.bootstrap,
oob_score=self.oob_score,
n_jobs=self.n_jobs,
random_state=self.random_state,
verbose=self.verbose,
warm_start=self.warm_start,
class_weight=self.class_weight,
)
# 2) fit it
self.forest.fit(X, y)
self.n_outputs_ = self.forest.n_outputs_
# 3) retrieve node norms and values
self.nodes_norm, self.nodes_value = compute_node_norm_classification_forest(
self.forest)
# 4) filter nodes
self._nodes_order = np.argsort(-self.nodes_norm)
if self.nodes_to_keep is not None:
if self.nodes_to_keep < 1:
nodes_to_keep = int(
len(self._nodes_order) * self.nodes_to_keep)
else:
nodes_to_keep = int(self.nodes_to_keep)
self._ind_nodes_to_keep = self._nodes_order[:nodes_to_keep]
else:
self._ind_nodes_to_keep = None
return self
def _set_nodes_to_keep(self, nodes_to_keep):
""" change the number of waweletts to keep withtout refitting the underlying random forest """
self.nodes_to_keep = nodes_to_keep
if self.forest is not None:
if self.nodes_to_keep is None:
self._ind_nodes_to_keep = None
else:
if self.nodes_to_keep < 1:
nodes_to_keep = int(
len(self._nodes_order) * self.nodes_to_keep)
else:
nodes_to_keep = int(self.nodes_to_keep)
self._ind_nodes_to_keep = self._nodes_order[:nodes_to_keep]
def predict_proba(self, X):
if self.forest is None:
raise NotFittedError("You should fit the model first")
path, _ = self.forest.decision_path(X)
if self._ind_nodes_to_keep is not None:
predict_proba_filtered = [
path[:, self._ind_nodes_to_keep].dot(
self.nodes_value[self._ind_nodes_to_keep, n, :])
for n in range(self.nodes_value.shape[1])
]
else:
predict_proba_filtered = [
path[:, :].dot(self.nodes_value[:, n, :])
for n in range(self.nodes_value.shape[1])
]
for p in predict_proba_filtered:
p[p < 0] = 0
p[p > 1] = 1
if len(predict_proba_filtered) == 1:
return predict_proba_filtered[0]
else:
return predict_proba_filtered
@property
def classes_(self):
return self.forest.classes_
def predict(self, X):
"""Predict class for X.
The predicted class of an input sample is a vote by the trees in
the forest, weighted by their probability estimates. That is,
the predicted class is the one with highest mean probability
estimate across the trees.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes.
"""
# Copied from base forest
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return self.classes_.take(np.argmax(proba, axis=1), axis=0)
else:
n_samples = proba[0].shape[0]
predictions = np.zeros((n_samples, self.n_outputs_))
for k in range(self.n_outputs_):
predictions[:, k] = self.classes_[k].take(np.argmax(proba[k],
axis=1),
axis=0)
return predictions
def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the trees in the
forest.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.
"""
# Copied from base forest
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return np.log(proba)
else:
for k in range(self.n_outputs_):
proba[k] = np.log(proba[k])
return proba
print X_test.head()
rf = RandomForestClassifier(n_estimators=30,
max_depth=None,
min_samples_split=10,
class_weight="balanced"
#min_weight_fraction_leaf=0.02
)
rf.fit(X_train, y_train)
print("\n\n ---Random Forest Model---")
rf_roc_auc = roc_auc_score(y_test, rf.predict(X_test))
print("Random Forest AUC = %2.2f" % rf_roc_auc)
rf_accuracy = accuracy_score(y_test, rf.predict(X_test))
print("Random Forest Accuracy= %2.2f" % rf_accuracy)
tn, fp, fn, tp = confusion_matrix(y_test, rf.predict(X_test)).ravel()
print "True -ve:", tn
print "True +ve:", tp
print "False -ve:", fn
print "False +ve:", fp
print(classification_report(y_test, rf.predict(X_test)))
print rf.predict_proba(X_test)
print confusion_matrix(y_test, rf.predict(X_test))
print len(rf.decision_path(X_test)[1])
from sklearn.externals import joblib
# save model
joblib.dump(rf, 'model.pkl')
combined_score = []
#function for returning for slicing condition
def condition_combination(index_vals, df=x):
condition = True
for indx in index_vals:
condition = condition & (df[features[indices[indx]]] >= min(feature_thresholds[indices[indx]][0])) & (df[features[indices[indx]]] <= max(feature_thresholds[indices[indx]][1]))
return condition
print("Feature ranking " + name + ":")
if(i < 3):
p = clf.predict(x)
temp = x[(p == 1) & (p == y)].copy()
temp = temp.to_numpy()
for n, row in enumerate(clf.decision_path(temp).toarray()):
for indx in np.nonzero(row)[-1][-2:-1]:
if(temp[n,clf.tree_.feature[indx]] <= clf.tree_.threshold[indx]):
feature_thresholds[clf.tree_.feature[indx]][1].append(clf.tree_.threshold[indx])
else:
feature_thresholds[clf.tree_.feature[indx]][0].append(clf.tree_.threshold[indx])
else:
for tree in clf.estimators_:
p = tree.predict(x)
temp = x[(p == 1) & (p == y)].copy()
temp = temp.to_numpy()
for n, row in enumerate(tree.decision_path(temp).toarray()):
for indx in np.nonzero(row)[-1][-2:-1]:
if(temp[n,tree.tree_.feature[indx]] <= tree.tree_.threshold[indx]):
feature_thresholds[tree.tree_.feature[indx]][1].append(tree.tree_.threshold[indx])
else:
print('Wrong value of codebook')
#fit the forest
forest = RandomForestClassifier(n_estimators=100, max_depth = 20, min_samples_split = 2,max_features = None, criterion = 'entropy', n_jobs = -1).fit(data_tr, labels)
#make predictions
hs = forest.predict(data_te)
#whats the accuracy
(forest.score(data_te, labels))*100
#how many matches is that
sum(labels == hs)
forest.decision_path(data_tr)
from IPython.display import display, Image
import pydotplus
import sklearn.tree as tree
for dtree in forest.estimators_:
dot_data = tree.export_graphviz(dtree
, out_file = None
, filled = True
, rounded = True
, special_characters = True)
graph = pydotplus.graph_from_dot_data(dot_data)
img = Image(graph.create_png())
display(img)
def Classifier_random_forest(Xfeat_test, Xfeat,y_each_patient_test, y_each_patient, selected_babies, \
selected_test, label,classweight, Used_classifier, drawing, lst, ChoosenKind,\
SamplingMeth,probability_threshold,ASprobLimit,N,crit,msl,deciding_performance_measure,dispinfo):
#### CREATING THE sampleweight FOR SELECTED BABIES
#### TRAIN CLASSIFIER
meanaccLOO = []
accLOO = []
testsubject = []
tpr_mean = []
counter = 0
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
F1_macro_collect = []
F1_micro_collect = []
F1_weight_collect = []
F1_all_collect = []
K_collect = []
preliminaryK = zeros(len(probthres_Grid))
#CREATING TEST AND TRAIN SETS
Selected_training = selected_babies
X_train = [
Xfeat[np.where(np.array(selected_babies) == k)[0][0]]
for k in Selected_training
] # combine only babies to train on in list
y_train = [
y_each_patient[np.where(np.array(selected_babies) == k)[0][0]]
for k in Selected_training
]
X_test = Xfeat_test[selected_test]
y_test = y_each_patient_test[selected_test]
X_train = vstack(
X_train) # mergin the data from each list element into one matrix
y_train = vstack(y_train)
y_old = y_train[:]
#SAMPLING TO EQUALIZE CLASS IMBALANCE
X_train, y_train = cmplx_Oversampling(X_train, y_train, ChoosenKind,
SamplingMeth, label)
#CALCULATE THE WEIGHTS DUE TO CLASS IMBALANCE
class_weight = 'balanced'
classlabels = ravel(
y_old) # y_test has to be a 1d array for compute_class_weight
# Now test if all labels are actually in the data. Otheriwse error with compute_class_weight. If not make the found labels the newe labels. If the new label is 1 then classsification does not work, therefore skip class_weigth , therefore CW
if (classweight == 1) and len(unique(classlabels)) == len(label):
cW = compute_class_weight(class_weight, label, classlabels)
cWdict = dict(
zip(label, cW)
) #the class weight need to be a dictionarry of the form:{class_label : value}
CW = 1
elif (classweight == 1) and len(unique(classlabels)) != len(label):
CW_label = unique(classlabels) #which values arein an array
if len(CW_label) == 1:
if dispinfo:
print('classweight config skiped once as only one class exist')
CW = 0
else:
if dispinfo:
print('used labels are:', CW_label, 'instead of:', label)
cW = compute_class_weight(class_weight, CW_label, classlabels)
cWdict = dict(
zip(label, cW)
) #the class weight need to be a dictionarry of the form:{class_label : value}
CW = 1
if dispinfo:
disp(cWdict)
#The Random Forest / Extreme Random Forest / Gradiant boosting
if Used_classifier == 'TR':
if (classweight == 1) and CW == 1:
clf = tree.DecisionTreeClassifier(criterion=crit, splitter="best", max_depth=None,\
min_samples_split=2, min_samples_leaf=msl, \
min_weight_fraction_leaf=0.0, max_features=None, \
random_state=42, max_leaf_nodes=None, min_impurity_decrease=0.0,\
min_impurity_split=None, class_weight=cWdict, presort=False)
else:
clf = tree.DecisionTreeClassifier(criterion=crit, splitter="best", max_depth=None,\
min_samples_split=2, min_samples_leaf=msl, \
min_weight_fraction_leaf=0.0, max_features=None, \
random_state=42, max_leaf_nodes=None, min_impurity_decrease=0.0,\
min_impurity_split=None, presort=False)
if Used_classifier == 'RF':
if (classweight == 1) and CW == 1:
clf = RandomForestClassifier(n_estimators=N, criterion=crit, max_depth=None, \
min_samples_split=2, min_samples_leaf=msl, min_weight_fraction_leaf=0.0,\
max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0,\
min_impurity_split=None, bootstrap=True, oob_score=False,\
n_jobs=1, random_state=42, verbose=0, warm_start=False,\
class_weight=cWdict)
else:
clf = RandomForestClassifier(n_estimators=N, criterion=crit, max_depth=None, \
min_samples_split=2, min_samples_leaf=msl, min_weight_fraction_leaf=0.0,\
max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0,\
min_impurity_split=None, bootstrap=True, oob_score=False,\
n_jobs=1, random_state=42, verbose=0, warm_start=False,\
)
elif Used_classifier == 'ERF':
if (classweight == 1) and CW == 1:
clf = ExtraTreesClassifier(n_estimators=N, criterion=crit, max_depth=None,\
min_samples_split=2, min_samples_leaf=msl, \
max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0,\
min_impurity_split=None, bootstrap=True, oob_score=False,\
n_jobs=1, random_state=42, verbose=0, warm_start=False,\
class_weight=cWdict)
else:
clf = ExtraTreesClassifier(n_estimators=N, criterion=crit, max_depth=None,\
min_samples_split=2, min_samples_leaf=msl, min_weight_fraction_leaf=0.0,\
max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0,\
min_impurity_split=None, bootstrap=True, oob_score=False,\
n_jobs=1, random_state=42, verbose=0, warm_start=False,\
)
elif Used_classifier == 'GB':
clf = GradientBoostingClassifier(loss="deviance", learning_rate=0.1, n_estimators=1000, subsample=1, \
criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,\
max_depth=30, min_impurity_decrease=0.0, min_impurity_split=None, init=None, \
random_state=42, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto')
elif Used_classifier == 'LR':
clf = LinearRegression(fit_intercept=True,
normalize=False,
copy_X=True,
n_jobs=1)
#Performance analysis
# sys.exit('Jan werth')
if len(label) < 2:
print("please use at least two labels")
# F1 Kappa
else:
prediction = clf.fit(X_train, y_train.ravel()).predict(
X_test) # prediction decide on 0.5 proability which class to take
probs = (
clf.fit(X_train, y_train.ravel()).predict_proba(X_test)
) # with the calculated probabilities we can choose our own threshold
if probability_threshold: # prediction takes always proability 0.5 to deside. Here we deside based on other way lower proabilities. deciding if any other than AS has slightly elevated probabilities
for k in range(len(probthres_Grid)
): # try differnt Trhesholds for the probability
preliminary_pred = copy(prediction[:])
probthres = probthres_Grid[k]
for i in range(len(probs)):
if len(label) == 3:
if any(
probs[i, 1:] >= probthres
) and probs[i, 0] < ASprobLimit[
0]: #IF THE PROBABILITY IS HIGHER THAN ... USE THAT CLASS INSTEAD OF AS. But if AS is over ~0.7 still take AS
highprob = np.argmax(
probs[i, 1:]
) # otherwise search for max prob of the labels other than AS
preliminary_pred[i] = label[
highprob +
1] # change the label in predictions to the new found label; +1 as we cut the array before by 1. Otherwise false index
if len(label) > 3:
if any(
probs[i, 1:] >= probthres
) and probs[i, 0] < ASprobLimit[
1]: #IF THE PROBABILITY IS HIGHER THAN ... USE THAT CLASS INSTEAD OF AS. But if AS is over ~0.7 still take AS
highprob = np.argmax(
probs[i, 1:]
) # otherwise search for max prob of the labels other than AS
preliminary_pred[i] = label[
highprob +
1] # change the label in predictions to the new found label; +1 as we cut the array before by 1. Otherwise false index
elif (
probs[i, 1]
) >= probthres: # if we have only two labels searching for max does not work
preliminary_pred[i] = label[
1] # CHange the label in prediction to the second label
#!!!!!!!! To change klassifier for perfomance measure
if deciding_performance_measure == 'Kappa':
preliminaryK[k] = cohen_kappa_score(
y_test.ravel(), preliminary_pred,
labels=label) # Find the threshold where Kapa gets max
elif deciding_performance_measure == 'F1_second_label':
preliminaryK[k] = f1_score(y_test.ravel(),
preliminary_pred,
labels=label,
average=None)[1]
elif deciding_performance_measure == 'F1_third_label':
preliminaryK[k] = f1_score(y_test.ravel(),
preliminary_pred,
labels=label,
average=None)[2]
elif deciding_performance_measure == 'F1_fourth_label':
preliminaryK[k] = f1_score(y_test.ravel(),
preliminary_pred,
labels=label,
average=None)[3]
#!!!!!!!! To change klassifier for perfomance measure
maxK = preliminaryK.argmax(axis=0)
if dispinfo:
print('Used probability Thresh: %.2f' % probthres_Grid[maxK])
probthres = probthres_Grid[
maxK] #repeat creating the predictions with the optimal probabilty threshold
for i in range(len(probs)):
if len(label) == 3:
if any(
probs[i, 1:] >= probthres
) and probs[i, 0] < ASprobLimit[
0]: #IF THE PROBABILITY IS HIGHER THAN ... USE THAT CLASS INSTEAD OF AS. But if AS is over ~0.7 still take AS
highprob = np.argmax(
probs[i, 1:]
) # otherwise search for max prob of the labels other than AS
prediction[i] = label[
highprob +
1] # change the label in predictions to the new found label; +1 as we cut the array before by 1. Otherwise false index
if len(label) > 3:
if any(
probs[i, 1:] >= probthres
) and probs[i, 0] < ASprobLimit[
1]: #IF THE PROBABILITY IS HIGHER THAN ... USE THAT CLASS INSTEAD OF AS. But if AS is over ~0.7 still take AS
highprob = np.argmax(
probs[i, 1:]
) # otherwise search for max prob of the labels other than AS
prediction[i] = label[highprob + 1] # cha
elif (
probs[i, 1]
) >= probthres: # if we have only two labels searching for max does not work
prediction[i] = label[
1] # CHange the label in prediction to the second label
scoring = clf.score(X_test, y_test.ravel(), sample_weight=None)
Fimportances = clf.feature_importances_
if Used_classifier != 'GB':
Dpath = clf.decision_path(X_train)
resultsF1_macro = f1_score(y_test.ravel(), prediction,
average='macro') #, pos_label=None)
resultsF1_micro = f1_score(y_test.ravel(), prediction, average='micro')
resultsF1_weight = f1_score(y_test.ravel(),
prediction,
average='weighted')
resultsF1_all = f1_score(y_test.ravel(),
prediction,
labels=label,
average=None) #, pos_label=None)
resultsK = cohen_kappa_score(y_test.ravel(), prediction, labels=label)
if drawing and Used_classifier == 'TR':
import graphviz
from Loading_5min_mat_files_cECG import Class_dict, features_dict
usedfeatures = list(
(features_dict[k]) for k in lst
) #create a dict only with the usedfeatures in lst out of all which are in features_dict
usedlabels = list(
(Class_dict[k]) for k in label
) #create a dict only with the usedfeatures in lst out of all which are in features_dict
with open("RF.txt", "w") as f:
f = tree.export_graphviz(clf,
out_file=f,
feature_names=usedfeatures,
class_names=usedlabels,
filled=True,
rounded=True)
# with open("RF.dot", "w") as f:
# f = tree.export_graphviz(clf, out_file=f)
with open("RF.svc", "w") as f:
f = tree.export_graphviz(clf, out_file=f)
# dot -Tpdf RF.dot -o RF.pdf
# open -a preview RF.pdf
# dot_data = tree.export_graphviz(clf, out_file=None)
# graph = graphviz.Source(dot_data)
# graph.render("Jan")
#
# dot_data = tree.export_graphviz(clf, out_file=None,
# feature_names=usedfeatures,
# class_names=usedlabels,
# filled=True, rounded=True,
# special_characters=True)
# graph = graphviz.Source(dot_data)
# graph
#
return resultsF1_macro, resultsK, resultsF1_micro, resultsF1_weight, resultsF1_all, Fimportances, scoring, prediction, probs
print(ypred) print(list(le.inverse_transform(ypred))) print(lgb1.predict_proba(xtest)) print(accuracy_score(ytest,ypred)) print(accuracy_score(ytrain,ypred1)) from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectFromModel rf=RandomForestClassifier(random_state=0,class_weight="balanced") print(rf.fit(xtrain,ytrain)) ypred=(rf.predict(xtest)) ypred1=(rf.predict(xtrain)) print(ypred) print(list(le.inverse_transform(ypred))) print(classification_report(ypred,ytest)) print(rf.apply(xtest)) print(rf.decision_path(xtest)) print(rf.predict_proba(xtest)) print(rf.predict_log_proba(xtest)) print(rf.score(xtrain,ytrain)) rmse=math.sqrt(mean_squared_error(ytest,ypred)) print(rmse) print(r2_score(ytest,ypred)) confusionmatrix=confusion_matrix(ypred,ytest) print(confusionmatrix) print(accuracy_score(ytest,ypred)) print(accuracy_score(ytrain,ypred1)) from sklearn.svm import SVC svc=SVC(kernel="rbf",random_state=0,gamma=1,C=1,class_weight="balanced") print(svc.fit(xtrain,ytrain)) ypred=svc.predict(xtest) ypred1=svc.predict(xtrain)
print(trainframe['label'])
trainframe['label'] = trainframe['label'].astype('int')
testframe['label'] = testframe['label'].astype('int')
print(testframe['label'])
with open('actualpredictionsRF', 'wb') as fp:
pickle.dump(testframe['label'], fp)
print("completed encoding labels")
# #clf = RandomForestClassifier()
#clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = RandomForestClassifier(n_estimators=200, max_depth=10, random_state=42)
clf = clf.fit(matrix, trainframe['label'])
#clf = clf.fit(matrix, trainframe['label'])
#predictions = clf.predict(matrix2)
#pickle to save the data
print(clf.decision_path(matrix))
filename = 'randomforestFinal'
pickle.dump(clf, open(filename, 'wb'))
loaded_model = pickle.load(open(filename, 'rb'))
predictions = loaded_model.predict(matrix2)
'''
filename1='RandomForestpredict'
pickle.dump(predictions,open(filename1, 'wb'))
'''
#print(clf.feature_importances_)
#predictions = clf.predict(matrix2)
ts2 = time.time()
ts2 = ts2 - ts
print("time taken")
print(ts2)
'oob_score': [True, False],
'random_state': [None, 2017]
}
RF_GS = RandomForestClassifier()
clf_GS_RF = GridSearchCV(RF_GS, RFparameters)
clf_GS_RF.fit(x_train_tfidf, y_train)
sorted(clf_GS_RF.cv_results_.keys())
GridPredRF = clf_GS_RF.predict(x_test_tfidf)
GS_RF_accuracy = metrics.accuracy_score(y_test, GridPredBag)
print('Accuracy score for clf_GS_RF: ', GS_RF_accuracy)
print('Best params for clf_GS_RF: ', clf_GS_RF.best_params_)
print('Detailed clf_GS_RF results: ', clf_GS_RF.cv_results_)
#print ('OOB score from clf_GS_RF : ', clf_GS_RF.oob_score_ )
#print ('OOB decision function from clf_GS_RF : ', RF_GS.oob_decision_function_ )
print('Feature importances from clf_GS_RF : ', RF_GS.feature_importances_)
RF_GS.decision_path(x_train_tfidf)
print(clf_GS_RF.best_params_)
print(clf_GS_RF.best_score_)
print(clf_GS_RF.grid_scores_)
#clf_GS_RF.error_score
print(metrics.confusion_matrix(y_test, GridPredRF))
#plt.plot( , clf_GS_RF)
for e in df['param_max_features'].value_counts().index:
print(e)
estimators = df.param_n_estimators[(df.param_criterion == 'entropy')
& (df.param_oob_score == True)]
mean_score = df.mean_test_score[(df.param_criterion == 'entropy')
& (df.param_oob_score == True)]
plt.plot(estimators[df['param_max_features'] == e],
mean_score[df['param_max_features'] == e],
plt.show()
#plt.plot()
y = train_data["Survived"]
#PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
features = ["Sex", "Age", "Pclass", "Embarked", "SibSp", "Parch"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])
model = RandomForestClassifier(n_estimators=100, max_depth=20, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
print(model.feature_importances_)
print(model.decision_path(X_test))
from sklearn.tree import export_graphviz
estimator = model.estimators_[5]
# Export as dot file
export_graphviz(estimator, out_file='tree.dot',
feature_names = ["Sex", "Age", "Pclass", "Embarked", "SibSp", "Parch"],
class_names = "Survived",
rounded = True, proportion = False,
precision = 2, filled = True)
# Convert to png using system command (requires Graphviz)
from subprocess import call
#process = subprocess.Popen(command, stdout=tempFile, shell=True)
#call(['dot', '-Tpng', 'tree.dot', '-o', 'tree.png', '-Gdpi=600'])
#set path=%path%;C:\Anaconda3\graphviz-2.38\release\bin
clf = RandomForestClassifier(random_state=0).fit(X_train, y_train)
print('RF Model fitted with' + repr(40) +
'features\n RF feature importances\n')
important_features = pd.Series(data=clf.feature_importances_,
index=fdataDF.columns)
feats = {} # a dict to hold feature_name: feature_importance
for feature, importance in zip(fdataDF.columns, clf.feature_importances_):
feats[feature] = importance #add the name/value pair
importances = pd.DataFrame.from_dict(
feats, orient='index').rename(columns={0: 'Gini-importance'})
importances.sort_values(by='Gini-importance').plot(kind='bar', rot=90)
plt.rcParams.update({'font.size': 5})
plt.show()
print('RF decision path')
pprint.pprint(clf.decision_path(X_train))
y_pred = clf.predict(X_test)
print('RF Score')
print(clf.score(X_test, y_test))
print('RMSE\n')
print(np.sqrt(metrics.mean_squared_error(y_pred, y_test)))
# computing permutation importance of each feature in dataset
perm = PermutationImportance(clf, random_state=1).fit(X_train, y_train)
# # create a structured array
# dtype = [('feature', str), ('permutation_weights', float)]
features = np.array(X_train.columns.to_list())
permF = np.array([X_train.columns.to_list, perm.feature_importances_])
rankedFeatureIds = perm.feature_importances_.argsort(
)[::
-1] #[::-1] reverses the ascending result of argsort, indices of arrays sorted
score = clf.score(X_test, y_test)
if score > current_best_score:
current_best_score = score
best_tree_shuffle = clf
print(current_best_score)
#%%
res = clf.predict(X_test)
compteur = 0
nb_nuls_predis = 0
for i in range(len(X_test)):
#print(res[i]==labels_test[i])
if res[i] == y_test[i]:
compteur += 1
#print('\n')
print(str(compteur * 100 / len(X_test)) + "%")
#%%
plot_confusion_matrix(clf, X_test, y_test)
#%%
clf.predict_proba(X_test)
clf.decision_path(X_test)
print(clf.decision_path(X_test))
clf.score(X_test, y_test)
#%% ne pas écraser foretV1 !!!
#from joblib import dump, load
#dump(clf, 'foretV2.joblib')
#%%
clf = load('foretV1.joblib')
#%%
clf.score(X_test, y_test)
#########################################
# Negative figures. We still have raw scores.
#######################################
# Option *decision_path*
# ++++++++++++++++++++++
#
# *scikit-learn* implements a function to retrieve the
# decision path. It can be enabled by option *decision_path*.
clrrf = RandomForestClassifier(n_estimators=2, max_depth=2)
clrrf.fit(X_train, y_train)
clrrf.predict(X_test[:2])
paths, n_nodes_ptr = clrrf.decision_path(X_test[:2])
print(paths.todense())
model_def = to_onnx(clrrf, X_train.astype(numpy.float32),
options={id(clrrf): {'decision_path': True,
'zipmap': False}})
sess = InferenceSession(model_def.SerializeToString())
##########################################
# The model produces 3 outputs.
print([o.name for o in sess.get_outputs()])
##########################################
# Let's display the last one.
# In[536]:
# Implies 300 Trees Generates the Highest Accuracy
plt.plot(xlabels, n_trees)
plt.xlabel('Trees in RandomForest')
plt.ylabel('Accuracy')
plt.title("RandomForest Optimization")
# #RandomForestClassifier(n_estimators=10, 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)
# In[514]:
# Why does this decision path look like its got more features than 5 ?
print(eeg_rf.score(test_x, test_y)*100)
eeg_rf.decision_path(test_x)
# ## Ensemble Classifiers
# In[64]:
# Voting Classifier ... Accuracy lower than RandomForest (with 500 trees) ...possible ?
voting = VotingClassifier(estimators=[('lr', lr_eeg), ('rf', eeg_rf), ('gnb', clf)], voting='hard')
voting = voting.fit(train_x_masked, train_y_masked)
print("Confusion Matrix ")
print("Classif: 0 1")
print(confusion_matrix(y_true=test_y_masked, y_pred=voting.predict(test_x_masked)))
print()
print("Voting Classifier Accuracy: ")
print((1-(902+289)/(666+902+289+1346))*100)
#To set the working directory
os.chdir("/Users/steven/Documents/dataMining/Kaggle/digitRecognizer")
cwd = os.getcwd()
# get titanic & test csv files as a DataFrame
digit_train_df = pd.read_csv("input/train.csv")
digit_test_df = pd.read_csv("input/test.csv")
# preview the data
digit_train_df.head()
digit_test_df.head()
#defining the training data set
X_train = digit_train_df.drop("label", axis=1)
Y_train = digit_train_df["label"]
X_test = digit_test_df
random_forest = RandomForestClassifier(n_estimators=10)
random_forest.fit(X_train, Y_train)
random_forest.decision_path(X_train)
Y_pred = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)
submission = pd.DataFrame({"ImageId": X_test.index + 1, "Label": Y_pred})
submission.to_csv('RF_10.csv', index=False)