def check_transformer_pickle(name, Transformer):
X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
random_state=0, n_features=2, cluster_std=0.1)
n_samples, n_features = X.shape
X = StandardScaler().fit_transform(X)
X -= X.min()
# catch deprecation warnings
with warnings.catch_warnings(record=True):
transformer = Transformer()
if not hasattr(transformer, 'transform'):
return
set_random_state(transformer)
set_fast_parameters(transformer)
# fit
if name in CROSS_DECOMPOSITION:
random_state = np.random.RandomState(seed=12345)
y_ = np.vstack([y, 2 * y + random_state.randint(2, size=len(y))])
y_ = y_.T
else:
y_ = y
transformer.fit(X, y_)
X_pred = transformer.fit(X, y_).transform(X)
pickled_transformer = pickle.dumps(transformer)
unpickled_transformer = pickle.loads(pickled_transformer)
pickled_X_pred = unpickled_transformer.transform(X)
assert_array_almost_equal(pickled_X_pred, X_pred)
def check_classifiers_classes(name, Classifier):
X, y = make_blobs(n_samples=30, random_state=0, cluster_std=0.1)
X, y = shuffle(X, y, random_state=7)
X = StandardScaler().fit_transform(X)
# We need to make sure that we have non negative data, for things
# like NMF
X -= X.min() - .1
y_names = np.array(["one", "two", "three"])[y]
for y_names in [y_names, y_names.astype('O')]:
if name in ["LabelPropagation", "LabelSpreading"]:
# TODO some complication with -1 label
y_ = y
else:
y_ = y_names
classes = np.unique(y_)
# catch deprecation warnings
with warnings.catch_warnings(record=True):
classifier = Classifier()
if name == 'BernoulliNB':
classifier.set_params(binarize=X.mean())
set_fast_parameters(classifier)
# fit
classifier.fit(X, y_)
y_pred = classifier.predict(X)
# training set performance
assert_array_equal(np.unique(y_), np.unique(y_pred))
if np.any(classifier.classes_ != classes):
print("Unexpected classes_ attribute for %r: "
"expected %s, got %s" %
(classifier, classes, classifier.classes_))
def loadData(path="../data/",k=5,log='add',pca_n=0,SEED=34):
from pandas import DataFrame, read_csv
from numpy import log as ln
from sklearn.cross_validation import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
train = read_csv(path+"train.csv")
test = read_csv(path+"test.csv")
id = test.id
target = train.target
encoder = LabelEncoder()
target_nnet = encoder.fit_transform(target).astype('int32')
feat_names = [x for x in train.columns if x.startswith('feat')]
train = train[feat_names].astype(float)
test = test[feat_names]
if log == 'add':
for v in train.columns:
train[v+'_log'] = ln(train[v]+1)
test[v+'_log'] = ln(test[v]+1)
elif log == 'replace':
for v in train.columns:
train[v] = ln(train[v]+1)
test[v] = ln(test[v]+1)
if pca_n > 0:
from sklearn.decomposition import PCA
pca = PCA(pca_n)
train = pca.fit_transform(train)
test = pca.transform(test)
scaler = StandardScaler()
scaler.fit(train)
train = DataFrame(scaler.transform(train),columns=['feat_'+str(x) for x in range(train.shape[1])])
test = DataFrame(scaler.transform(test),columns=['feat_'+str(x) for x in range(train.shape[1])])
cv = KFold(len(train), n_folds=k, shuffle=True, random_state=SEED)
return train, test, target, target_nnet, id, cv, encoder
def transformTestData(self, train_data, test_data):
#Select the right features for both training and testing data
X_train, y_train = self.__selectRelevantFeatures(train_data)
X_test, y_test = self.__selectRelevantFeatures(test_data)
#Transform categorical variables into integer labels
martial_le = LabelEncoder()
occupation_le = LabelEncoder()
relationship_le = LabelEncoder()
race_le = LabelEncoder()
sex_le = LabelEncoder()
transformers = [martial_le, occupation_le, relationship_le, race_le, sex_le]
for i in range(len(transformers)):
X_train[:,i] = transformers[i].fit_transform(X_train[:,i])
X_test[:,i] = transformers[i].transform(X_test[:,i])
#Dummy code categorical variables
dummy_code = OneHotEncoder(categorical_features = range(5))
X_train = dummy_code.fit_transform(X_train).toarray()
X_test = dummy_code.transform(X_test).toarray()
#Normalize all features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
#Encode y
class_le = LabelEncoder()
y_train = class_le.fit_transform(y_train)
y_test = class_le.transform(y_test)
#print class_le.transform(["<=50K", ">50K"])
return X_train, X_test, y_train, y_test
def clustering_approach(self):
'''
Cluster user data using various clustering algos
IN: self.df_full and self.labels
OUT: results to stdout
'''
print 'Fitting clustering model'
X = self.df_full.values
y = self.labels
# scale data
scaler = StandardScaler()
X = scaler.fit_transform(X)
# KMeans
km_clf = KMeans(n_clusters=2, n_jobs=6)
km_clf.fit(X)
# swap labels as super-users are in cluster 0 (messy!!)
temp = y.apply(lambda x: 0 if x == 1 else 1)
print '\nKMeans clustering: '
self.analyse_preds(temp, km_clf.labels_)
# Agglomerative clustering
print '\nAgglomerative clustering approach: '
ac_clf = AgglomerativeClustering()
ac_labels = ac_clf.fit_predict(X)
self.analyse_preds(y, ac_labels)
return None
def check_clustering(name, Alg):
X, y = make_blobs(n_samples=50, random_state=1)
X, y = shuffle(X, y, random_state=7)
X = StandardScaler().fit_transform(X)
n_samples, n_features = X.shape
# catch deprecation and neighbors warnings
with warnings.catch_warnings(record=True):
alg = Alg()
set_fast_parameters(alg)
if hasattr(alg, "n_clusters"):
alg.set_params(n_clusters=3)
set_random_state(alg)
if name == 'AffinityPropagation':
alg.set_params(preference=-100)
alg.set_params(max_iter=100)
# fit
alg.fit(X)
# with lists
alg.fit(X.tolist())
assert_equal(alg.labels_.shape, (n_samples,))
pred = alg.labels_
assert_greater(adjusted_rand_score(pred, y), 0.4)
# fit another time with ``fit_predict`` and compare results
if name is 'SpectralClustering':
# there is no way to make Spectral clustering deterministic :(
return
set_random_state(alg)
with warnings.catch_warnings(record=True):
pred2 = alg.fit_predict(X)
assert_array_equal(pred, pred2)
def check_transformer_general(name, Transformer):
X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
random_state=0, n_features=2, cluster_std=0.1)
X = StandardScaler().fit_transform(X)
X -= X.min()
_check_transformer(name, Transformer, X, y)
_check_transformer(name, Transformer, X.tolist(), y.tolist())
def cross_valid(data, classifier, x_cols, y_col, **kwargs): # Do train-test split for cross-validation size = len(data) kf = train_test_split(size) y_pred = np.zeros(size) y_pred_prob = np.zeros(size) y = data[y_col].as_matrix().astype(np.float) totaltime_train = 0 totaltime_test = 0 for train_index, test_index in kf: # Fill in missing values df = data.copy() df = fill_missing_median(df, train_index) # Transform and normalize X = df[x_cols].as_matrix().astype(np.float) scaler = StandardScaler() X = scaler.fit_transform(X) # Build classifier and yield predictions y_pred[test_index], y_pred_prob[test_index], train_time, test_time \ = model(X, y, train_index, test_index, classifier, **kwargs) totaltime_train += train_time totaltime_test += test_time avgtime_train = train_time/len(kf) avgtime_test = test_time/len(kf) return y, y_pred, y_pred_prob, avgtime_train, avgtime_test
def buildTreeRegressor(predictorColumns, structurestable = 'structures.csv', targetcolumn = 'c_a', md = None):
"""
Build a random forest-regressor model to predict some structure feature from compositional data. Will return the model trained on all data, a mean_absolute_error score, and a table of true vs. predicted values
"""
df = pd.read_csv(structurestable)
df = df.dropna()
if('fracNobleGas' in df.columns):
df = df[df['fracNobleGas'] <= 0]
s = StandardScaler()
X = s.fit_transform(df[predictorColumns].astype('float64'))
y = df[targetcolumn].values
rfr = RandomForestRegressor(max_depth = md)
acc = mean(cross_val_score(rfr, X, y, scoring=make_scorer(mean_absolute_error)))
X_train, X_test, y_train, y_test = train_test_split(X,y)
rfr.fit(X_train,y_train)
y_predict = rfr.predict(X_test)
t = pd.DataFrame({'True':y_test, 'Predicted':y_predict})
rfr.fit(X, y)
return rfr, t, round(acc,2)
def buildTreeClassifier(predictorColumns, structurestable = 'structures.csv', targetcolumn = 'pointGroup', md = None):
"""
Build a random forest-classifier model to predict some structure feature from compositional data. Will return the model trained on all data, a confusion matrix calculated , and an average accuracy score. Also returns a label encoder object
"""
df = pd.read_csv(structurestable)
df = df.dropna()
if('fracNobleGas' in df.columns):
df = df[df['fracNobleGas'] <= 0]
s = StandardScaler()
le = LabelEncoder()
X = s.fit_transform(df[predictorColumns].astype('float64'))
y = le.fit_transform(df[targetcolumn].values)
rfc = RandomForestClassifier(max_depth = md)
acc = mean(cross_val_score(rfc, X, y))
X_train, X_test, y_train, y_test = train_test_split(X,y)
rfc.fit(X_train,y_train)
y_predict = rfc.predict(X_test)
cm = confusion_matrix(y_test, y_predict)
cm = pd.DataFrame(cm, columns=le.classes_, index=le.classes_)
rfc.fit(X, y)
return rfc, cm, round(acc,2), le
def buildCoordinationTreeRegressor(predictorColumns, element, coordinationDir = 'coordination/', md = None):
"""
Build a coordination predictor for a given element from compositional structure data of structures containing that element. Will return a model trained on all data, a mean_absolute_error score, and a table of true vs. predicted values
"""
try:
df = pd.read_csv(coordinationDir + element + '.csv')
except Exception:
print 'No data for ' + element
return None, None, None
df = df.dropna()
if('fracNobleGas' in df.columns):
df = df[df['fracNobleGas'] <= 0]
if(len(df) < 4):
print 'Not enough data for ' + element
return None, None, None
s = StandardScaler()
X = s.fit_transform(df[predictorColumns].astype('float64'))
y = df['avgCoordination'].values
rfr = RandomForestRegressor(max_depth = md)
acc = mean(cross_val_score(rfr, X, y, scoring=make_scorer(mean_absolute_error)))
X_train, X_test, y_train, y_test = train_test_split(X,y)
rfr.fit(X_train,y_train)
y_predict = rfr.predict(X_test)
t = pd.DataFrame({'True':y_test, 'Predicted':y_predict})
rfr.fit(X, y)
return rfr, t, round(acc,2)
def main():
t0 = time.time() # start time
# output files path
TRAINX_OUTPUT = "../../New_Features/train_x_processed.csv"
TEST_X_OUTPUT = "../../New_Features/test__x_processed.csv"
# input files path
TRAIN_FILE_X1 = "../../ML_final_project/sample_train_x.csv"
TRAIN_FILE_X2 = "../../ML_final_project/log_train.csv"
TEST__FILE_X1 = "../../ML_final_project/sample_test_x.csv"
TEST__FILE_X2 = "../../ML_final_project/log_test.csv"
# load files
TRAIN_DATA_X1 = np.loadtxt(TRAIN_FILE_X1, delimiter=',', skiprows=1, usecols=(range(1, 18)))
TEST__DATA_X1 = np.loadtxt(TEST__FILE_X1, delimiter=',', skiprows=1, usecols=(range(1, 18)))
TRAIN_DATA_X2 = logFileTimeCount(np.loadtxt(TRAIN_FILE_X2, delimiter=',', skiprows=1, dtype=object))
TEST__DATA_X2 = logFileTimeCount(np.loadtxt(TEST__FILE_X2, delimiter=',', skiprows=1, dtype=object))
# combine files
TRAIN_DATA_X0 = np.column_stack((TRAIN_DATA_X1, TRAIN_DATA_X2))
TEST__DATA_X0 = np.column_stack((TEST__DATA_X1, TEST__DATA_X2))
# data preprocessing
scaler = StandardScaler()
TRAIN_DATA_X = scaler.fit_transform(TRAIN_DATA_X0)
TEST__DATA_X = scaler.transform(TEST__DATA_X0)
# output processed files
outputXFile(TRAINX_OUTPUT, TRAIN_DATA_X)
outputXFile(TEST_X_OUTPUT, TEST__DATA_X)
t1 = time.time() # end time
print "...This task costs " + str(t1 - t0) + " second."
def knn(x_train, y_train, x_valid):
x_train=np.log(x_train+1)
x_valid=np.log(x_valid+1)
where_are_nan = np.isnan(x_train)
where_are_inf = np.isinf(x_train)
x_train[where_are_nan] = 0
x_train[where_are_inf] = 0
where_are_nan = np.isnan(x_valid)
where_are_inf = np.isinf(x_valid)
x_valid[where_are_nan] = 0
x_valid[where_are_inf] = 0
scale=StandardScaler()
scale.fit(x_train)
x_train=scale.transform(x_train)
x_valid=scale.transform(x_valid)
#pca = PCA(n_components=10)
#pca.fit(x_train)
#x_train = pca.transform(x_train)
#x_valid = pca.transform(x_valid)
kneighbors=KNeighborsClassifier(n_neighbors=200,n_jobs=-1)
knn_train, knn_test = stacking(kneighbors, x_train, y_train, x_valid, "knn")
return knn_train, knn_test, "knn"
def test_transformers_data_not_an_array():
# test if transformers do something sensible on training set
# also test all shapes / shape errors
transformers = all_estimators(type_filter='transformer')
X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
random_state=0, n_features=2, cluster_std=0.1)
X = StandardScaler().fit_transform(X)
# We need to make sure that we have non negative data, for things
# like NMF
X -= X.min() - .1
for name, Transformer in transformers:
# XXX: some transformers are transforming the input
# data. This is a bug that we'll fix later. Right now we copy
# the data each time
this_X = NotAnArray(X.copy())
this_y = NotAnArray(np.asarray(y))
if name in dont_test:
continue
# these don't actually fit the data:
if name in ['AdditiveChi2Sampler', 'Binarizer', 'Normalizer']:
continue
# And these wan't multivariate output
if name in ('PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD'):
continue
yield check_transformer, name, Transformer, this_X, this_y
def load_train_data(path):
print("Loading Train Data")
df = pd.read_csv(path)
# Remove line below to run locally - Be careful you need more than 8GB RAM
rows = np.random.choice(df.index.values, 40000)
df = df.ix[rows]
# df = df.sample(n=40000)
# df = df.loc[df.index]
labels = df.target
df = df.drop('target',1)
df = df.drop('ID',1)
# Junk cols - Some feature engineering needed here
df = df.fillna(-1)
X = df.values.copy()
np.random.shuffle(X)
X = X.astype(np.float32)
encoder = LabelEncoder()
y = encoder.fit_transform(labels).astype(np.int32)
scaler = StandardScaler()
X = scaler.fit_transform(X)
return X, y, encoder, scaler
class Regressor(BaseEstimator):
def __init__(self):
self.clf = Pipeline([
("RF", RandomForestRegressor(n_estimators=200, max_depth=15,
n_jobs=N_JOBS))])
self.scaler = StandardScaler()
self.agglo = FeatureAgglomeration(n_clusters=500)
def fit(self, X, y):
y = y.ravel()
n_samples, n_lags, n_lats, n_lons = X.shape
self.scaler.fit(X[:, -1].reshape(n_samples, -1))
X = X.reshape(n_lags * n_samples, -1)
connectivity = grid_to_graph(n_lats, n_lons)
self.agglo.connectivity = connectivity
X = self.scaler.transform(X)
X = self.agglo.fit_transform(X)
X = X.reshape(n_samples, -1)
self.clf.fit(X, y)
def predict(self, X):
n_samples, n_lags, n_lats, n_lons = X.shape
X = X.reshape(n_lags * n_samples, -1)
X = self.scaler.transform(X)
X = self.agglo.transform(X)
X = X.reshape(n_samples, -1)
return self.clf.predict(X)
def linregress(X_train, X_test, y_train, y_test):
coef = []
for col in X_train.columns.tolist():
X = StandardScaler().fit_transform(X_train[col])
lr = LinearRegression()
lr.fit(X.reshape(-1, 1), y_train)
coef.append([col, lr.coef_])
coef = sorted(coef, key=lambda x: x[1])[::-1]
nos = [x[1] for x in coef]
labs = [x[0] for x in coef]
for lab in labs:
if lab == 'doubles':
labs[labs.index(lab)] = '2B'
elif lab == 'triples':
labs[labs.index(lab)] = '3B'
elif lab == 'Intercept':
idx = labs.index('Intercept')
labs.pop(idx)
nos.pop(idx)
labs = [lab.upper() for lab in labs]
x = range(len(nos))
plt.plot(x,nos, lw=2, c='b')
plt.xticks(x, labs)
plt.title('Linear Regression Coefficients (Win Percentage)')
plt.savefig('images/coefficients.png')
plt.show()
print labs
def monary_load(start=0,stop=-1, find_args={}, species_to_retrieve=[]):
if species_to_retrieve == []:
species_to_retrieve = species
else:
species_to_retrieve = [s for s in species_to_retrieve if s in species]
query = {}
for s in species_to_retrieve:
query[s] = {"$gt": 0}
find_args["$or"] = [{k:query[k]} for k in query.keys()]
with Monary("127.0.0.1") as monary:
out = monary.query(
"creeval",
collection,
find_args,
num_metadata+cat_metadata+species_to_retrieve,
["float32"] * (len(num_metadata)+len(cat_metadata)+len(species_to_retrieve)),
limit=(stop-start),
offset=start
)
for i,col in enumerate(out[0:len(num_metadata+cat_metadata)]):
out[i] = np.ma.filled(col,np.ma.mean(col))
#if any(np.isnan(col)):
# print col
out = np.ma.row_stack(out).T
X = out[:,0:len(num_metadata+cat_metadata)]
y = out[:,len(num_metadata+cat_metadata):]
y = (y > 0).astype(int)
scaler = StandardScaler().fit(X)
X = scaler.transform(X)
pickle.dump(scaler,open(collection+"_scaler.pkl","wb"))
y = np.asarray(y)
return DenseDesignMatrix(X=X,y=y)
class PCATransform(BaseEstimator, TransformerMixin):
"""
PCA with an argument that allows the user to skip the transform
altogether.
"""
def __init__(self, n_components=.1, skip=False, whiten=False, standard_scalar=True):
print 'PCA!'
self.n_components = n_components
self.skip = skip
self.whiten = whiten
self.standard_scalar = standard_scalar
def fit(self, X, y=None):
if not self.skip:
if self.standard_scalar:
self.std_scalar = StandardScaler().fit(X)
X = self.std_scalar.transform(X)
self.pca = PCA(n_components=self.n_components, whiten=self.whiten).fit(X)
return self
def transform(self, X, y=None):
if not self.skip:
if self.standard_scalar:
X = self.std_scalar.transform(X)
return self.pca.transform(X)
return X
def main(trainFile, testFile, outputFile, mode, classifier):
"""
input:
1. trainFile: the training data features file
2. testFile: the test data file
3. outputFile: the file where the output of the test data has to be written
4. classifier: the classifier to be used
"""
# scale the input data
scaler = StandardScaler()
trainingData = getData(trainFile)
trainX = trainingData[0]
trainY = trainingData[1]
trainX = scaler.fit_transform(trainX)
testX = []
testY = []
# train the classifier
clf = trainClassifier(trainX, trainY, classifier, mode)
# if test mode, get test data and predict the output classes
if mode == 1:
testData = getData(testFile)
testX = testData[0]
testY = testData[1]
testX = scaler.transform(testX)
actY = test(testX, clf)
testY = testY.reshape(len(testY), 1)
# write the predicted class probabilities
output = np.concatenate((testY, actY), axis = 1)
np.savetxt(outputFile, output, fmt='%s', delimiter=',')
def process(discrete, cont): # Create discrete and continuous data matrices discrete_X = np.array(discrete) cont_X = np.array(cont) # Impute discrete values imp = Imputer(strategy='most_frequent') discrete_X = imp.fit_transform(discrete_X) # Impute continuous values imp_c = Imputer(strategy='mean') cont_X = imp_c.fit_transform(cont_X) # Discrete basis representation enc = OneHotEncoder() enc.fit(discrete_X) discrete_X = enc.transform(discrete_X).toarray() # Continuous scaling scaler = StandardScaler() scaler.fit(cont_X) cont_X = scaler.transform(cont_X) # Merge to one array X = np.concatenate((discrete_X, cont_X), axis=1) return X
class Classifier(BaseEstimator):
def __init__(self):
self.label_encoder = LabelEncoder()
self.scaler = StandardScaler()
self.clf = None
def fit(self, X, y):
X = self.scaler.fit_transform(X.astype(np.float32))
y = self.label_encoder.fit_transform(y).astype(np.int32)
dtrain = xgb.DMatrix( X, label=y.astype(np.float32))
param = {'objective':'multi:softprob', 'eval_metric':'mlogloss'}
param['nthread'] = 4
param['num_class'] = 9
param['colsample_bytree'] = 0.55
param['subsample'] = 0.85
param['gamma'] = 0.95
param['min_child_weight'] = 3.0
param['eta'] = 0.05
param['max_depth'] = 12
num_round = 400 # to be faster ??
#num_round = 820
self.clf = xgb.train(param, dtrain, num_round)
def predict(self, X):
X = self.scaler.transform(X.astype(np.float32))
dtest = xgb.DMatrix(X)
label_index_array = np.argmax(self.clf.predict(dtest), axis=1)
return self.label_encoder.inverse_transform(label_index_array)
def predict_proba(self, X):
X = self.scaler.transform(X.astype(np.float32))
dtest = xgb.DMatrix(X)
return self.clf.predict(dtest)
def normalize( training_data, test_data ): scaler = StandardScaler() values = scaler.fit_transform( training_data ) training_data = pd.DataFrame( values, columns=training_data.columns, index=training_data.index ) values = scaler.transform( test_data ) test_data = pd.DataFrame( values, columns=test_data.columns, index=test_data.index ) return training_data, test_data
def load_data_csv_advanced(datafile):
"""
Loads data from given CSV file. The first line in the given CSV file is expected to be the names of the columns.
:param datafile: path of the file
:return: a NumPy array containing a data point in each row
"""
# File format for CSV file. For example, setting _X_COLUMN to 'x' means that x coordinates of geographical location
# will be at the column named 'x' in the CSV file.
_COLUMN_X = 'x'
_COLUMN_Y = 'y'
data = pd.read_csv(datafile)
# Normalize
scaler = StandardScaler()
scaler.fit(data[[_COLUMN_X, _COLUMN_Y]])
data[[_COLUMN_X, _COLUMN_Y]] = scaler.transform(data[[_COLUMN_X, _COLUMN_Y]])
# Get feature vector names by removing "x" and "y"
feature_vector_names = data.columns.difference([_COLUMN_X, _COLUMN_Y])
data_coords = data[[_COLUMN_X, _COLUMN_Y]].values
result = {"coordinates": data_coords}
for feature in feature_vector_names:
data_words = [[e.strip() for e in venue_data.split(",")] for venue_data in data[feature].values.flatten().tolist()]
result[feature] = data_words
return sparsify_data(result, None, None), scaler # None for both params since SVD is not used
def lassoRegression(X,y):
print("\n### ~~~~~~~~~~~~~~~~~~~~ ###")
print("Lasso Regression")
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
myDegree = 40
polynomialFeatures = PolynomialFeatures(degree=myDegree, include_bias=False)
Xp = polynomialFeatures.fit_transform(X)
myScaler = StandardScaler()
scaled_Xp = myScaler.fit_transform(Xp)
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
lassoRegression = Lasso(alpha=1e-7)
lassoRegression.fit(scaled_Xp,y)
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
dummyX = np.arange(0,2,0.01)
dummyX = dummyX.reshape((dummyX.shape[0],1))
dummyXp = polynomialFeatures.fit_transform(dummyX)
scaled_dummyXp = myScaler.transform(dummyXp)
dummyY = lassoRegression.predict(scaled_dummyXp)
outputFILE = 'plot-lassoRegression.png'
fig, ax = plt.subplots()
fig.set_size_inches(h = 6.0, w = 10.0)
ax.axis([0,2,0,15])
ax.scatter(X,y,color="black",s=10.0)
ax.plot(dummyX, dummyY, color='red', linewidth=1.5)
plt.savefig(filename = outputFILE, bbox_inches='tight', pad_inches=0.2, dpi = 600)
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
return( None )
def prepare_features(data, enc=None, scaler=None):
'''
One-hot encode all boolean/string (categorical) features,
and shift/scale integer/float features
'''
# X needs to contain only non-negative integers
bfs = data['bfeatures'] + 1
sfs = data['sfeatures'] + 1
# Shift/scale integer and float features to have mean=0, std=1
ifs = data['ifeatures']
ffs = data['ffeatures']
x2 = np.hstack((ifs,ffs))
if scaler is None:
scaler = StandardScaler()
x2 = scaler.fit_transform(x2)
print "Training features have mean: %s" % scaler.mean_
print "and standard deviation: %s" % scaler.std_
else:
x2 = scaler.transform(x2, copy=False)
# one-hot encode categorical features
X = np.hstack((bfs,sfs,x2))
categorical = np.arange(bfs.shape[1]+sfs.shape[1])
if enc is None:
enc = OneHotEncoder(n_values='auto', categorical_features=categorical)
X = enc.fit_transform(X)
print "One-hot encoded features have dimension %d" % X.shape[1]
else:
X = enc.transform(X)
return X, enc, scaler
def load_data_csv(datafile):
"""
Loads data from given CSV file. The first line in the given CSV file is expected to be the names of the columns.
:param datafile: path of the file
:return: a NumPy array containing a data point in each row
"""
# File format for CSV file. For example, setting _X_COLUMN to 'x' means that x coordinates of geographical location
# will be at the column named 'x' in the CSV file.
# This will be useful later when we start adding more features.
_COLUMN_X = 'x'
_COLUMN_Y = 'y'
_COLUMN_W = 'color'
data = pd.read_csv(datafile)
# Normalize
scaler = StandardScaler()
scaler.fit(data[[_COLUMN_X, _COLUMN_Y]])
data[[_COLUMN_X, _COLUMN_Y]] = scaler.transform(data[[_COLUMN_X, _COLUMN_Y]])
data_coords = data[[_COLUMN_X, _COLUMN_Y]].values
data_words = [[e] for e in data[[_COLUMN_W]].values.flatten().tolist()]
data = {"coordinates": data_coords, "words": data_words}
return sparsify_data(data, None, None), scaler # None for both params since SVD is not used
def run_model( model, model_name, X, Y, X_val):
new_values = [ [x] for x in range(len(X))]
X = numpy.append(X, new_values, 1)
from sklearn.preprocessing import StandardScaler # I have a suspicion that the classifier might work better without the scaler
scaler = StandardScaler().fit(X)
X = scaler.transform(X)
max_time_val = X[-1][-1] *2 - X[-2][-1]
Y = make_black_maps_class(Y)
# Load validation data
model.fit(X, Y)
new_values = [ [max_time_val] for x in range(len(X_val))]
X_val = numpy.append(X_val, new_values, 1)
# Now predict validation output
Y_pred = model.predict(X_val)
# Crop impossible values
Y_pred[Y_pred < 0] = 0
Y_pred[Y_pred > 600] = 600
savetxt('final_pred_y{0}.csv'.format(model_name), Y_pred, delimiter=',')
black_map_count = 0
for y in Y_pred:
if y == 600:
black_map_count += 1
print black_map_count, model_name
sys.stdout.flush()
def test_scaler_1d():
"""Test scaling of dataset along single axis"""
rng = np.random.RandomState(0)
X = rng.randn(5)
X_orig_copy = X.copy()
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
# check inverse transform
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_array_almost_equal(X_scaled_back, X_orig_copy)
# Test with 1D list
X = [0., 1., 2, 0.4, 1.]
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
X_scaled = scale(X)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
def train_and_test(train_books, test_books, train, scale=True):
X_train, y_train, cands_train, features = get_pair_data(train_books, True)
X_test, y_test, cands_test, features = get_pair_data(test_books)
scaler = None
if scale:
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
print sum(y_train)*0.1/len(y_train)
print 'Start training'
print X_train.shape
clf = train(X_train, y_train)
print 'Done training'
y_train_pred = clf.predict(X_train)
y_test_pred = clf.predict(X_test)
'''
# print performance for training books
print "--------------Traning data-------------"
train_perf = evaluate_books(clf, train_books, scaler, evaluate_pair)
# print performance for testing books
print "\n"
print "--------------Testing data-------------"
test_perf = evaluate_books(clf, test_books, scaler, evaluate_pair)
'''
print 'Train Non-unique Precision:', precision(y_train_pred, y_train), 'Non-unique Recall:', recall(y_train_pred, y_train)
print 'Test Non-unique Precision:', precision(y_test_pred, y_test), 'Recall:', recall(y_test_pred, y_test)
return clf, scaler, X_train, y_train, X_test, y_test
X.columns[feats.get_support()]
############ SEQUENTIAL FEATURE SELECTION #################
#Includes Forward Selection vs Backward selection
from sklearn.neighbors import KNeighborsClassifier
from mlxtend.feature_selection import SequentialFeatureSelector
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
knn_pipe = Pipeline([('scaler', StandardScaler()), ('knn', KNeighborsClassifier(n_neighbors=4, n_jobs=-1))])
# Forward Selection
selector = SequentialFeatureSelector(knn_pipe, scoring='accuracy', forward=True,
floating=False, k_features=3,
verbose=2, n_jobs=-1, cv=5)
selector.fit(X=X, y=y)
selector.subsets_
selector.k_feature_idx_
selector.k_feature_names_
def run_sim(n, path, simtitle, REVOLUTION, PROBLEM, seed):
POP_SIZE = 100
MAX_GENERATION = 50
MAX_EPISODE = 100
MAX_EVAL = 1000
STOPPING_RULE = 'max_eval'
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [1., 1.]
MINIMIZE = True
VERBOSE = True
X_SCALER = StandardScaler()
# Set global numpy random_state
np.random.seed(seed)
theo = PROBLEM.solutions()
# Instantiate a population
pop = MOPRISM(size=POP_SIZE,
problem=PROBLEM,
max_generation=MAX_GENERATION,
max_episode=MAX_EPISODE,
reference=REF,
minimize=MINIMIZE,
stopping_rule=STOPPING_RULE,
max_eval=MAX_EVAL,
mutation_rate=MUTATION_RATE,
revolution=REVOLUTION,
embedded_ea=MOEAD,
verbose=VERBOSE,
no_improvement_step_tol=3)
pop.selection_fun = pop.compute_front
pop.mutation_fun = gaussian_mutator
pop.crossover_fun = random_crossover
# Parametrization
params_ea = {
'u': MUTATION_U,
'st': MUTATION_ST,
'trial_method': 'lhs',
'trial_criterion': 'cm'
}
kernel = CubicKernel
tail = LinearTail
params_surrogate = \
{'kernel': kernel,
'tail': tail,
'maxp': MAX_EVAL + POP_SIZE,
'eta': 1e-8,
}
# ===============================Initialization============================
pop.config_surrogate(typ='rbf',
params=params_surrogate,
n_process=1,
X_scaler=X_SCALER,
warm_start=True)
pop.config_gap_opt(at='least_crowded',
radius=0.1,
size=POP_SIZE,
max_generation=MAX_GENERATION,
selection_fun=None,
mutation_fun=None,
mutation_rate=None,
crossover_fun=random_crossover,
trial_method='lhs',
trial_criterion='cm',
u=0.,
st=0.2)
pop.config_sampling(methods='default',
sizes='default',
rate='default',
candidates='default')
pop.run(params_ea=params_ea, params_surrogate=params_surrogate, theo=theo)
# ============================= Save Results ================================ #
# path to save
directory = path + simtitle + '/' + str(n) + '/'
if not os.path.exists(directory):
os.makedirs(directory)
pop.render_features(pop.true_front).tofile(directory + 'xs.dat')
pop.render_targets(pop.true_front).tofile(directory + 'fs.dat')
np.array(pop.hypervol_diff).tofile(directory + 'hv_diff.dat')
np.array(pop.hypervol_cov_effect).tofile(directory + 'hv_cov.dat')
np.array(pop.hypervol_index).tofile(directory + 'hv_ind.dat')
# ================================Visualization============================== #
# plot_res(pop=pop, ref=theo, directory=directory)
return pop
from sklearn.decomposition import PCA
from sklearn.decomposition import IncrementalPCA
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
if __name__ == "__main__":
dt_heart = pd.read_csv("./data/heart.csv")
print(dt_heart.head(5))
dt_features = dt_heart.drop(["target"], axis=1)
dt_target = dt_heart["target"]
dt_features = StandardScaler().fit_transform(dt_features)
X_train, X_test, y_train, y_test = train_test_split(dt_features,
dt_target,
test_size=0.3,
random_state=42)
print(X_train.shape, y_train.shape)
pca = PCA(n_components=3)
pca.fit(X_train)
ipca = IncrementalPCA(n_components=3, batch_size=10)
ipca.fit(X_train)
#plt.plot(range(len(pca.explained_variance_)), pca.explained_variance_ratio_)
def one_day_window_model():
files = glob.glob('../DATA/A*/A*/*.json')
sentimentAnalyzer = SentimentIntensityAnalyzer()
with open('tweet_sentiment.csv', 'w+') as sfl:
for file in files:
with open(file) as fl:
lines = fl.readlines()
tweets = json.loads(lines[0])
for tweet in tweets:
date = time.strftime('%Y/%m/%d',
time.localtime(int(tweet['time'])))
scores = sentimentAnalyzer.polarity_scores(tweet['text'])
sfl.write(date + ',' + str(scores['pos']) + ',' +
str(scores['neg']) + ',' + str(scores['neu']) +
',' + str(scores['compound']))
sfl.write('\n')
prices = pd.read_csv('../DATA/CHARTS/APPLE1440.csv').values
all_tweets = pd.read_csv('tweet_sentiment.csv').values
with open('features.csv', 'w+') as fl:
for price in prices:
current_date = datetime.strptime(price[0], '%Y.%m.%d').date()
previous_date = current_date - timedelta(days=1)
tweets = all_tweets[all_tweets[:, 0] == previous_date.strftime(
'%Y/%m/%d')]
if len(tweets) != 0:
if float(price[5]) > float(price[2]):
label = "1"
else:
label = "0"
for tweet in tweets:
fl.write(price[0] + ',' + str(tweet[1]) + ',' +
str(tweet[2]) + ',' + str(tweet[3]) + ',' +
str(tweet[4]) + ',' + label)
fl.write('\n')
dataset = pd.read_csv('features.csv')
X = dataset.iloc[:, [1, 2, 3, 4]].values
y = dataset.iloc[:, 5].values
scaler = StandardScaler()
X[:, :] = scaler.fit_transform(X[:, :])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
rf = RandomForestClassifier(n_estimators=500,
criterion='entropy',
max_depth=3)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print("Random Forest Accuracy: " +
str((cm[0, 0] + cm[1, 1]) /
(cm[0, 0] + cm[1, 1] + cm[1, 0] + cm[0, 1])))
svc = SVC(kernel='poly', random_state=0)
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print("SVM Accuracy: " + str((cm[0, 0] + cm[1, 1]) /
(cm[0, 0] + cm[1, 1] + cm[1, 0] + cm[0, 1])))
mlp = MLPClassifier(hidden_layer_sizes=(100, 100, 100, 100),
random_state=10)
mlp.fit(X_train, y_train)
y_pred = mlp.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print("MLP Accuracy: " + str((cm[0, 0] + cm[1, 1]) /
(cm[0, 0] + cm[1, 1] + cm[1, 0] + cm[0, 1])))
X_train_norm = mms.fit_transform(X_train)
print(X_train_norm)
X_test_norm = mms.transform(X_test)
print(X_test)
# 标准化对于许多线性模型都十分有必要
print('standardized:', (ex - ex.mean()) / ex.std())
from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)
# 选择有意义的特征
# 正则化
print('normalized:', (ex - ex.min()) / (ex.max() - ex.min()))
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(penalty='l1', C=1.0)
lr.fit(X_train_std, y_train)
import DatabaseConnection as dc
# Connect to database and get the data
home_data = dc.download_housing_data(dc.connect())
# Filter out outliers in the dataset based on the outlier graph from preprocessing
# indicating that the majority of the data is in the lower end of the price range.
home_data = home_data[home_data['price'] < 1250000]
# Visualize the different groupings of houses in the dataset using kmeans cluster analysis.
cata_home_data = home_data.copy() # copy of the dataset to maintain integrity
price_data = cata_home_data['price']
cata_home_data.drop(['price'],axis=1,inplace=True)
# Standardize the data.
scaler = StandardScaler()
scaler.fit(cata_home_data)
scaled_data = scaler.transform(cata_home_data)
# Determine the obtimal number of clusters
def show_elbow_plot():
"""
Displays the elbow graph used to choose the optimal number of clusters for the final cluster graph.
"""
test_cluster_max = 15
kmeans_tests = [KMeans(n_clusters=i) for i in range(1, test_cluster_max)]
score = [kmeans_tests[i].fit(scaled_data).score(scaled_data) for i in range(len(kmeans_tests))]
# Plot the curve
elbow_plot = plt.plot(range(1, test_cluster_max),score)
#%%
# lets do PCA
sns.set_style("darkgrid")
colors = ['#e6194b',
'#0082c8',
'#d2f53c',
'#3cb44b',
'#f032e6',
'#911eb4',
'#46f0f0',
'#f58231',
'#008080',
'#ffe119']
scalar = StandardScaler()
X_train = scalar.fit_transform(X_train)
# visulize all attributes in the data set
num_components = 2
pca = PCA(n_components = num_components)
pca.fit(X_train)
print(pca.explained_variance_ratio_)
print('var', sum(pca.explained_variance_ratio_))
total_explained_variance = sum(pca.explained_variance_ratio_)
train_pca = pca.transform(X_train) # make this more pretty l8ter
records, attributes = np.shape(train_pca)
train_pca_ones = np.ones((records, attributes + 1))
train_pca_ones[:,1:] = train_pca
def validateRF():
"""
run KFOLD method for regression
"""
#defining directories
dir_in = "/lustre/fs0/home/mtadesse/merraAllLagged"
dir_out = "/lustre/fs0/home/mtadesse/merraRFValidation"
surge_path = "/lustre/fs0/home/mtadesse/05_dmax_surge_georef"
#cd to the lagged predictors directory
os.chdir(dir_in)
x = 119
y = 120
#empty dataframe for model validation
df = pd.DataFrame(columns = ['tg', 'lon', 'lat', 'num_year', \
'num_95pcs','corrn', 'rmse'])
#looping through
for tg in range(x, y):
os.chdir(dir_in)
#filter only .csv files
tgNames = []
for file in glob.glob("*.csv"):
tgNames.append(file)
tg_name = sorted(tgNames)[tg]
print(tg_name)
##########################################
#check if this tg is already taken care of
##########################################
os.chdir(dir_out)
if os.path.isfile(tg_name):
print("this tide gauge is already taken care of")
return "file already analyzed!"
os.chdir(dir_in)
#load predictor
pred = pd.read_csv(tg_name)
pred.drop('Unnamed: 0', axis=1, inplace=True)
#add squared and cubed wind terms (as in WPI model)
pickTerms = lambda x: x.startswith('wnd')
wndTerms = pred.columns[list(map(pickTerms, pred.columns))]
wnd_sqr = pred[wndTerms]**2
wnd_cbd = pred[wndTerms]**3
pred = pd.concat([pred, wnd_sqr, wnd_cbd], axis=1)
#standardize predictor data
dat = pred.iloc[:, 1:]
scaler = StandardScaler()
print(scaler.fit(dat))
dat_standardized = pd.DataFrame(scaler.transform(dat), \
columns = dat.columns)
pred_standardized = pd.concat([pred['date'], dat_standardized], axis=1)
#load surge data
os.chdir(surge_path)
surge = pd.read_csv(tg_name)
surge.drop('Unnamed: 0', axis=1, inplace=True)
#remove duplicated surge rows
surge.drop(surge[surge['ymd'].duplicated()].index,
axis=0,
inplace=True)
surge.reset_index(inplace=True)
surge.drop('index', axis=1, inplace=True)
#adjust surge time format to match that of pred
time_str = lambda x: str(datetime.strptime(x, '%Y-%m-%d'))
surge_time = pd.DataFrame(list(map(time_str, surge['ymd'])),
columns=['date'])
time_stamp = lambda x: (datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
surge_new = pd.concat([surge_time, surge[['surge', 'lon', 'lat']]],
axis=1)
#merge predictors and surge to find common time frame
pred_surge = pd.merge(pred_standardized,
surge_new.iloc[:, :2],
on='date',
how='right')
pred_surge.sort_values(by='date', inplace=True)
#find rows that have nans and remove them
row_nan = pred_surge[pred_surge.isna().any(axis=1)]
pred_surge.drop(row_nan.index, axis=0, inplace=True)
pred_surge.reset_index(inplace=True)
pred_surge.drop('index', axis=1, inplace=True)
#in case pred and surge don't overlap
if pred_surge.shape[0] == 0:
print('-' * 80)
print('Predictors and Surge don' 't overlap')
print('-' * 80)
continue
pred_surge['date'] = pd.DataFrame(list(map(time_stamp, \
pred_surge['date'])), \
columns = ['date'])
#prepare data for training/testing
X = pred_surge.iloc[:, 1:-1]
y = pd.DataFrame(pred_surge['surge'])
y = y.reset_index()
y.drop(['index'], axis=1, inplace=True)
#apply PCA
pca = PCA(.95)
pca.fit(X)
X_pca = pca.transform(X)
#apply 10 fold cross validation
kf = KFold(n_splits=10, random_state=29)
metric_corr = []
metric_rmse = []
#combo = pd.DataFrame(columns = ['pred', 'obs'])
for train_index, test_index in kf.split(X):
X_train, X_test = X_pca[train_index], X_pca[test_index]
y_train, y_test = y['surge'][train_index], y['surge'][test_index]
#train regression model
rf= RandomForestRegressor(n_estimators = 50, random_state = 101, \
min_samples_leaf = 1)
rf.fit(X_train, y_train)
#predictions
predictions = rf.predict(X_test)
# pred_obs = pd.concat([pd.DataFrame(np.array(predictions)), \
# pd.DataFrame(np.array(y_test))], \
# axis = 1)
# pred_obs.columns = ['pred', 'obs']
# combo = pd.concat([combo, pred_obs], axis = 0)
#evaluation matrix - check p value
if stats.pearsonr(y_test, predictions)[1] >= 0.05:
print("insignificant correlation!")
continue
else:
print(stats.pearsonr(y_test, predictions))
metric_corr.append(stats.pearsonr(y_test, predictions)[0])
print(np.sqrt(metrics.mean_squared_error(y_test, predictions)))
print()
metric_rmse.append(
np.sqrt(metrics.mean_squared_error(y_test, predictions)))
#number of years used to train/test model
num_years = (pred_surge['date'][pred_surge.shape[0]-1] -\
pred_surge['date'][0]).days/365
longitude = surge['lon'][0]
latitude = surge['lat'][0]
num_pc = X_pca.shape[1] #number of principal components
corr = np.mean(metric_corr)
rmse = np.mean(metric_rmse)
print('num_year = ', num_years, ' num_pc = ', num_pc ,'avg_corr = ',np.mean(metric_corr), ' - avg_rmse (m) = ', \
np.mean(metric_rmse), '\n')
#original size and pca size of matrix added
new_df = pd.DataFrame(
[tg_name, longitude, latitude, num_years, num_pc, corr, rmse]).T
new_df.columns = ['tg', 'lon', 'lat', 'num_year', \
'num_95pcs','corrn', 'rmse']
df = pd.concat([df, new_df], axis=0)
#save df as cs - in case of interruption
os.chdir(dir_out)
df.to_csv(tg_name)
def train_and_predict_dragons(t,
y_unscaled,
x,
targeted_regularization=True,
output_dir='',
knob_loss=dragonnet_loss_binarycross,
ratio=1.,
dragon='',
val_split=0.2,
batch_size=64):
verbose = 0
y_scaler = StandardScaler().fit(y_unscaled)
y = y_scaler.transform(y_unscaled)
train_outputs = []
test_outputs = []
if dragon == 'tarnet':
dragonnet = make_tarnet(x.shape[1], 0.01)
elif dragon == 'dragonnet':
print("I am here making dragonnet")
dragonnet = make_dragonnet(x.shape[1], 0.01)
metrics = [
regression_loss, binary_classification_loss, treatment_accuracy,
track_epsilon
]
if targeted_regularization:
loss = make_tarreg_loss(ratio=ratio, dragonnet_loss=knob_loss)
else:
loss = knob_loss
# for reporducing the IHDP experimemt
i = 0
tf.random.set_seed(i)
np.random.seed(i)
# print()
train_index, test_index = train_test_split(np.arange(x.shape[0]),
random_state=1)
test_index = train_index
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
t_train, t_test = t[train_index], t[test_index]
yt_train = np.concatenate([y_train, t_train], 1)
import time
start_time = time.time()
dragonnet.compile(optimizer=Adam(lr=1e-3), loss=loss, metrics=metrics)
adam_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=2, min_delta=0.),
ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=5,
verbose=verbose,
mode='auto',
min_delta=1e-8,
cooldown=0,
min_lr=0)
]
dragonnet.fit(x_train,
yt_train,
callbacks=adam_callbacks,
validation_split=val_split,
epochs=100,
batch_size=batch_size,
verbose=verbose)
sgd_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=40, min_delta=0.),
ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=5,
verbose=verbose,
mode='auto',
min_delta=0.,
cooldown=0,
min_lr=0)
]
sgd_lr = 1e-5
momentum = 0.9
dragonnet.compile(optimizer=SGD(lr=sgd_lr,
momentum=momentum,
nesterov=True),
loss=loss,
metrics=metrics)
dragonnet.fit(x_train,
yt_train,
callbacks=sgd_callbacks,
validation_split=val_split,
epochs=300,
batch_size=batch_size,
verbose=verbose)
elapsed_time = time.time() - start_time
print("***************************** elapsed_time is: ", elapsed_time)
yt_hat_test = dragonnet.predict(x_test)
yt_hat_train = dragonnet.predict(x_train)
test_outputs += [
_split_output(yt_hat_test, t_test, y_test, y_scaler, x_test,
test_index)
]
train_outputs += [
_split_output(yt_hat_train, t_train, y_train, y_scaler, x_train,
train_index)
]
K.clear_session()
return test_outputs, train_outputs
# Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Random Forest Classification to the Training set
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=10,
criterion='entropy',
random_state=0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
#### Initialize Dataset ####
data_df = pd.read_csv(settings['dataset_file'])
data = {
'outcome_name': data_df.columns[0],
'variable_names': data_df.columns[1:].tolist(),
'X': data_df.iloc[:, 1:],
'y': data_df.iloc[:, 0],
'scaler': None,
}
if settings['normalize_data']:
from sklearn.preprocessing import StandardScaler
data['scaler'] = StandardScaler(copy = True, with_mean = True, with_std = True)
data['X_train'] = pd.DataFrame(data['scaler'].fit_transform(data['X'], data['y']), columns = data['X'].columns)
else:
data['X_train'] = data['X']
#### Initialize Actionset ####
default_bounds = (0.1, 99.9, 'percentile')
custom_bounds = None
immutable_variables = []
if settings['data_name'] == 'credit':
immutable_names = ['Female', 'Single', 'Married']
immutable_names += list(filter(lambda x: 'Age' in x or 'Overdue' in x, data['variable_names']))
def train_and_predict_ned(t,
y_unscaled,
x,
targeted_regularization=True,
output_dir='',
knob_loss=dragonnet_loss_binarycross,
ratio=1.,
dragon='',
val_split=0.2,
batch_size=64):
verbose = 0
y_scaler = StandardScaler().fit(y_unscaled)
y = y_scaler.transform(y_unscaled)
train_outputs = []
test_outputs = []
nednet = make_ned(x.shape[1], 0.01)
metrics_ned = [ned_loss]
metrics_cut = [regression_loss]
# for reproducing the ihdp result
i = 0
tf.random.set_random_seed(i)
np.random.seed(i)
# change the test_size to get in sample and out sample estimates
test_size = 0.
train_index, test_index = train_test_split(np.arange(x.shape[0]),
test_size=test_size)
if test_size == 0:
test_index = train_index
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
t_train, t_test = t[train_index], t[test_index]
yt_train = np.concatenate([y_train, t_train], 1)
nednet.compile(optimizer=Adam(lr=1e-3), loss=ned_loss, metrics=metrics_ned)
adam_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=2, min_delta=0.),
ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=5,
verbose=verbose,
mode='auto',
min_delta=1e-8,
cooldown=0,
min_lr=0)
]
nednet.fit(x_train,
yt_train,
callbacks=adam_callbacks,
validation_split=val_split,
epochs=100,
batch_size=batch_size,
verbose=verbose)
sgd_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=40, min_delta=0.),
ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=5,
verbose=verbose,
mode='auto',
min_delta=0.,
cooldown=0,
min_lr=0)
]
sgd_lr = 1e-5
momentum = 0.9
nednet.compile(optimizer=SGD(lr=sgd_lr, momentum=momentum, nesterov=True),
loss=ned_loss,
metrics=metrics_ned)
print(nednet.summary())
nednet.fit(x_train,
yt_train,
callbacks=sgd_callbacks,
validation_split=val_split,
epochs=300,
batch_size=batch_size,
verbose=verbose)
t_hat_test = nednet.predict(x_test)[:, 1]
t_hat_train = nednet.predict(x_train)[:, 1]
# cutting the activation layer
cut_net = post_cut(nednet, x.shape[1], 0.01)
cut_net.compile(optimizer=Adam(lr=1e-3),
loss=dead_loss,
metrics=metrics_cut)
adam_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=2, min_delta=0.),
ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=5,
verbose=verbose,
mode='auto',
min_delta=1e-8,
cooldown=0,
min_lr=0)
]
cut_net.fit(x_train,
yt_train,
callbacks=adam_callbacks,
validation_split=val_split,
epochs=100,
batch_size=batch_size,
verbose=verbose)
sgd_callbacks = [
TerminateOnNaN(),
EarlyStopping(monitor='val_loss', patience=40, min_delta=0.),
ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=5,
verbose=verbose,
mode='auto',
min_delta=0.,
cooldown=0,
min_lr=0)
]
sgd_lr = 1e-5
momentum = 0.9
cut_net.compile(optimizer=SGD(lr=sgd_lr, momentum=momentum, nesterov=True),
loss=dead_loss,
metrics=metrics_cut)
cut_net.fit(x_train,
yt_train,
callbacks=sgd_callbacks,
validation_split=val_split,
epochs=300,
batch_size=batch_size,
verbose=verbose)
y_hat_test = cut_net.predict(x_test)
y_hat_train = cut_net.predict(x_train)
yt_hat_test = np.concatenate([y_hat_test, t_hat_test.reshape(-1, 1)], 1)
yt_hat_train = np.concatenate([y_hat_train, t_hat_train.reshape(-1, 1)], 1)
test_outputs += [
_split_output(yt_hat_test, t_test, y_test, y_scaler, x_test,
test_index)
]
train_outputs += [
_split_output(yt_hat_train, t_train, y_train, y_scaler, x_train,
train_index)
]
K.clear_session()
return test_outputs, train_outputs
# ### Regression # ##### I think that this problem may be best generalized with a random forest model. First I will start with a regressor, then I will use a classifier from sklearn.ensemble import RandomForestRegressor from sklearn.svm import SVR from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, classification_report, confusion_matrix from sklearn.preprocessing import StandardScaler X = df[['temperature', 'humidity', 'IsHoliday', 'WeekDay', 'Season']] y = df['P1'] today = [12, 47, 0, 1, 1] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) sc_regr = StandardScaler() X_train = sc_regr.fit_transform(X_train) X_test = sc_regr.transform(X_test) today = sc_regr.transform([today]) regr = RandomForestRegressor() regr_svm = SVR() regr.fit(X_train, y_train) regr_svm.fit(X_train, y_train) regr.predict(today) regr_svm.predict(today)
print (f)
x = getDummy(x, f)
test = x.iloc[260753:, ]
train = x.iloc[:260753:, ]
encoder = LabelEncoder()
y_train = encoder.fit_transform(y_train).astype(np.int32)
y_train = np_utils.to_categorical(y_train)
print ("processsing finished")
train = np.array(train)
train = train.astype(np.float32)
test = np.array(test)
test = test.astype(np.float32)
if need_normalise:
scaler = StandardScaler().fit(train)
train = scaler.transform(train)
test = scaler.transform(test)
# folds
xfolds = pd.read_csv(projPath + 'input/xfolds.csv')
# work with 5-fold split
fold_index = xfolds.fold5
fold_index = np.array(fold_index) - 1
n_folds = len(np.unique(fold_index))
nb_classes = 2
print(nb_classes, 'classes')
dims = train.shape[1]
print(dims, 'dims')
)
vals = {}
for i, name in enumerate(names):
vals[name] = X[:, i]
vals[dataset.default_target_attribute] = y
df = pd.DataFrame(vals)
X = df.drop(task_target, axis=1)
y = df.loc[:, task_target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
#:# preprocessing
transform_pipeline = Pipeline([
('scaler', StandardScaler())
])
X_train = pd.DataFrame(transform_pipeline.fit_transform(X_train), columns=X_train.columns)
#:# model
params = {'C': 0.8, 'solver': 'liblinear'}
classifier = LogisticRegression(**params)
classifier.fit(X_train, y_train)
#:# hash
#:# 8b4aedd7d78f7193c71d75501c5c1bc6
md5 = hashlib.md5(str(classifier).encode('utf-8')).hexdigest()
print(f'md5: {md5}')
xx.append([])
for j in [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]:
xx[i].append(data[i][j])
x = np.array(xx)
y = np.array(yy)
# print(len(x),len(y))
# 2 分割训练数据和测试数据
# 随机采样25%作为测试 75%作为训练
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.25,
random_state=33)
# 3 训练数据和测试数据进行标准化处理
ss_x = StandardScaler()
x_train = ss_x.fit_transform(x_train)
x_test = ss_x.transform(x_test)
ss_y = StandardScaler()
y_train = ss_y.fit_transform(y_train.reshape(-1, 1))
y_test = ss_y.transform(y_test.reshape(-1, 1))
# 4 使用回归树进行训练和预测
# 初始化k近邻回归模型 使用平均回归进行预测
dtr = DecisionTreeRegressor()
# 训练
dtr.fit(x_train, y_train)
# 预测 保存预测结果
dtr_y_predict = dtr.predict(x_test)
np.random.seed(seed=0) random_state = 0 ####### # DADOS: ####### # a) para aplicacao com dados 'reais', utilizamos o Wine dataset: # Nesse caso, apos gerar os dados abaixo, rodar o codigo somente a partir da linha 54 ateh 227, para evitar as replicados da simulacao de monte carlo. wine = datasets.load_wine() X = wine.data y = wine.target data = DataFrame(X) # padronizacao das variaveis para ficarem em uma mesma escala: scaler = StandardScaler() data = DataFrame(scaler.fit_transform(data), index=data.index, columns=data.columns) # visualizacao dos dados apenas ateh a quarta variavel, pra nao poluir tanto o grafico: plot = sns.pairplot(data.iloc[:,0:4]) cols = data.columns # ou: # b) com dados simulados: # quantidade de replicacoes: reps = 1000 acertou = [] for rep in tqdm(range(reps)):
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import *
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
#data = pd.read_csv("../../data/data_all_float.csv", header=0, index_col=None, sep=';')
# drop 'CTE, linear' for more instances to study
drop_feature = ['Density', 'CTE, linear']
for ifeature in drop_feature:
data = data.drop(labels=ifeature, axis=1)
stdscale = StandardScaler()
elements = [
'Iron, Fe', 'Carbon, C', 'Sulfur, S', 'Silicon, Si', 'Phosphorous, P',
'Manganese, Mn', 'Chromium, Cr', 'Nickel, Ni', 'Molybdenum, Mo',
'Copper, Cu'
]
target = 'Thermal Conductivity'
# drop instances with NaN
drop_instance = []
for idx in data.index:
if math.isnan(data.loc[idx, target]):
drop_instance.append(idx)
data_loc = data.drop(drop_instance)
X = data_loc[elements].values
def pls_variable_selection(x, y, num_pls_components):
"""
Adopted from https://nirpyresearch.com/variable-selection-method-pls-python/
:param x:
:param y:
:param max_components:
:param scorer:
:return:
"""
# initialize new model parameter holder
scores = dict()
scores['r2'] = ModelFit()
scores['mae'] = ModelFit()
cut_conditions = []
num_varaibles = []
# make a score table to fill in
# scores = np.zeros( x.shape[1] )
# print('==========')
pls = PLSRegression(num_pls_components)
usable_columns = None
best_score = 0
x_scaled_np = StandardScaler().fit_transform(x)
x_scaled = pd.DataFrame(x_scaled_np, columns=x.columns)
# print(x_scaled)
while x_scaled.shape[1] >= num_pls_components:
print('shape: ', x_scaled.shape, num_pls_components, best_score)
number_to_cut = int(x_scaled.shape[1] / 100)
if number_to_cut == 0:
number_to_cut = 1
# print('number to cut: ', number_to_cut, num_pls_components)
pls.fit(x_scaled, y)
# y_predict = pls.predict(x_scaled)
# score = r2_score(y, y_predict)
cv_splitter = 3 # passing to corss_validate will implement a KFold with 3 folds
group_splitter = None
if x_scaled.shape[1] <= 200:
# cv_splitter = ShuffleSplit(n_splits=100, test_size=0.35)
cv_splitter = GroupShuffleSplit(n_splits=100, test_size=0.35)
group_splitter = data_full['Leaf number']
elif x_scaled.shape[1] <= 400:
# cv_splitter = ShuffleSplit(n_splits=30, test_size=0.35)
cv_splitter = GroupShuffleSplit(n_splits=30, test_size=0.35)
group_splitter = data_full['Leaf number']
local_scores = cross_validate(
pls,
x_scaled,
y,
cv=cv_splitter,
return_train_score=True,
groups=group_splitter,
scoring=['r2', 'neg_mean_absolute_error'])
scores['r2'].train_score.append(local_scores['train_r2'].mean())
scores['r2'].train_stdev.append(local_scores['train_r2'].std())
scores['r2'].test_score.append(local_scores['test_r2'].mean())
scores['r2'].test_stdev.append(local_scores['test_r2'].std())
scores['mae'].train_score.append(
local_scores['train_neg_mean_absolute_error'].mean())
scores['mae'].train_stdev.append(
local_scores['train_neg_mean_absolute_error'].std())
scores['mae'].test_score.append(
local_scores['test_neg_mean_absolute_error'].mean())
scores['mae'].test_stdev.append(
local_scores['test_neg_mean_absolute_error'].std())
num_varaibles.append(x_scaled.shape[1])
if scores['r2'].test_score[-1] > best_score:
best_score = scores['r2'].test_score[-1]
usable_columns = x_scaled.columns
# print(pls.coef_[:, 0])
# print(pls.coef_.shape)
sorted_coeff = np.argsort(np.abs(pls.coef_[:, 0]))
# print('1')
# print(sorted_coeff)
# print( pls.coef_[:, 0][sorted_coeff] )
# print('2')
# print(sorted_coeff[-5:])
# print(sorted_coeff[-1])
# print(x_scaled)
# print(pls.coef_[:, 0][sorted_coeff[-1]], pls.coef_[:, 0][sorted_coeff[0]])
# print(sorted_coeff[-1], x_scaled.columns[sorted_coeff[0]])
# print(scores['r2'].train_score[-1], scores['r2'].test_score[-1],
# scores['mae'].train_score[-1], scores['mae'].test_score[-1])
# column_to_drop = x_scaled.columns[sorted_coeff[0]]
columns_to_drop = x_scaled.columns[sorted_coeff[:number_to_cut]]
# print(columns_to_drop.values)
if x_scaled.shape[1] < 50:
# print('dropping: ', columns_to_drop)
# print(columns_to_drop.values)
cut_conditions.append(columns_to_drop.values)
x_scaled.drop(columns=columns_to_drop, inplace=True)
# print(usable_columns)
# print('===========')
# print(x_scaled.columns)
# print(cut_conditions)
# data = dict()
# data['test means'] = test_scores_average
# data['test std'] = test_scores_std
# data['train means'] = train_scores_average
# data['train std'] = train_scores_std
# data['columns'] = usable_columns
# data['num variables'] = num_varaibles
scores['columns'] = usable_columns
scores['num variables'] = num_varaibles
# print('========')
# print(data.keys())
# filename = "param_selector_{0}.pickle".format(num_pls_components)
# with open(filename, 'wb') as f:
# pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
return scores
# Get dataset and features
#==============================#
aalist = list('ACDEFGHIKLMNPQRSTVWY')
def getAAC(seq):
aac = np.array([seq.count(x) for x in aalist]) / len(seq)
return aac
data = pd.read_excel('sequence_ogt_topt.xlsx', index_col=0)
aac = np.array([getAAC(seq) for seq in data['sequence']])
ogt = data['ogt'].values.reshape((data.shape[0], 1))
X = np.append(aac, ogt, axis=1)
sc = StandardScaler()
X = sc.fit_transform(X)
y = data['topt'].values
# Strategies and hyperparameters
#======================================#
# Hyperparameter range
cl_vals = [25.0, 30.0, None]
ch_vals = [72.2, 60.0]
ks = [5, 10, 15]
deltas = [0.1, 0.5, 1.0]
overs = [0.5, 0.75]
unders = [0.5, 0.75]
sizes = [300, 600]
sample_methods = ['balance', 'extreme', 'average']
def featureScal(X):
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X)
return X_train
xgb_preds = np.expm1(model_xgb.predict(X_test))
lasso_preds = np.expm1(model_lasso.predict(X_test))
predictions = pd.DataFrame({"xgb":xgb_preds, "lasso":lasso_preds})
predictions.plot(x = "xgb", y = "lasso", kind = "scatter")
preds = 0.7*lasso_preds + 0.3*xgb_preds
solution = pd.DataFrame({"id":test.Id, "SalePrice":preds})
#solution.to_csv("lasso_xgb.csv", index = False)
from keras.layers import Dense
from keras.models import Sequential
from keras.regularizers import l1
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
X_train = StandardScaler().fit_transform(X_train)
X_tr, X_val, y_tr, y_val = train_test_split(X_train, y, random_state = 3)
print(X_tr.shape)
X_tr
model = Sequential()
#model.add(Dense(256, activation="relu", input_dim = X_train.shape[1]))
model.add(Dense(1, input_dim = X_train.shape[1], W_regularizer=l1(0.001)))
model.compile(loss = "mse", optimizer = "adam")
model.summary()
hist = model.fit(X_tr, y_tr, validation_data = (X_val, y_val))
pd.Series(model.predict(X_val)[:,0]).hist()
from flask import Flask, render_template, request
import pickle
from sklearn.preprocessing import StandardScaler
app = Flask(__name__)
classifier = pickle.load(open('score.pkl', 'rb'))
@app.route('/', methods=['GET'])
def home():
return render_template('index.html')
standard_to = StandardScaler()
@app.route("/predict", methods=['POST'])
def predict():
if request.method == 'POST':
grescore = int(request.form['grescore'])
toeflscore = int(request.form['toeflscore'])
rating = request.form['rating']
SOP = float(request.form['sop'])
LOR = float(request.form['lor'])
CGPA = float(request.form['cgpa'])
Research = request.form['Research']
if Research == 'yes':
Research = 1
else:
Research = 0
# import data
train = get_feature_data()
df_orig = get_original_data()
# Whether to vectorize original data or not
vectorise_text = True
if vectorise_text == True:
vectorized_desciption = vectorize_text_feature(train, 'Description',15)
vectorized_title = vectorize_text_feature(train, 'Country', 5)
train2 = train.drop(['InvoiceDate'],axis=1)
train2 = pd.concat([train2, vectorized_desciption, vectorized_title], axis=1)
else:
train2 = train.drop(['InvoiceDate'],axis=1)
# standardize data
scaler = StandardScaler()
scaler = scaler.fit(train2)
train_scaled = scaler.transform(train2)
# use principle component analysis to reduce dimensions
pca = PCA(n_components=20)
pca = pca.fit(train_scaled)
train_reduced = pca.transform(train_scaled)
# calculate matrix of cosine similarity
distances = cosine_similarity(train_reduced).T
# find top 5 closest ads
top_5_orig = []
top_distances = []
for i in range(len(train_reduced)):
def bayesian_regression_modeling(df,
label_col,
target_col,
prior_distribution_list,
draw_sample=1000,
chains=2,
scaling_opt=3):
"""
:param df:
:param label_col:
:param target_col:
:param model_option:
:param draw_sample:
:param chains:
:param alpha_1:
:param alpha_2:
:param lambda_1:
:param lambda_2:
:return: MCMC mean trace array, MCMC visualization img source
"""
# test: using scaling
n_individuals = len(df)
print("scale@@@@@",df)
if scaling_opt == 1:
df[df.columns] = StandardScaler().fit_transform(df[df.columns])
elif scaling_opt == 2:
df[df.columns] = MinMaxScaler().fit_transform(df[df.columns])
elif scaling_opt == 3:
df[df.columns] = df[df.columns]
feature_list = df.columns
feature_list = [i for i in feature_list if i != label_col]
print(feature_list)
print("Model datasett:BHJLK")
print(df)
# Using PyMC3
# formula = str(target_col)+' ~ '+' + '.join(['%s' % variable for variable in label_col])
# degree of freedom
# nu = len(df[label_col].count(axis=1)) - len(df[label_col].count(axis=0))
# TODO: add student-T distribution as priors for each feature
# get degree of freedom
if len(df[label_col].count(axis=1)) >= len(df[label_col].count(axis=0)):
nu = len(df[label_col].count(axis=1)) - len(df[label_col].count(axis=0))
else:
nu = 0
print("Degree of Freedom:")
print(nu)
# with pm.Model() as normal_model:
# start = time.time()
#
# # intercept = pm.StudentT('Intercept', nu=nu, mu=0, sigma=1)
# # sigma = pm.HalfCauchy("sigma", beta=10, testval=1.)
# # pass in a prior mean & coefficient list
#
#
# family = pm.glm.families.Normal()
# pm.GLM.from_formula(formula, data=df, family=family)
# trace = pm.sample(draws=draw_sample, chains=chains, tune=500, random_seed=23)
# end = time.time()
# print("Time elasped: {} seconds.".format(end-start))
# print("DONE normal_trace")
with pm.Model() as model:
fea_list = [variable for variable in label_col]
print(fea_list)
pm_list = []
i = 0
for prior_dist in prior_distribution_list:
# for j in range(len(fea_list)):
for type,prior in prior_dist.items():
if type != "Target" and prior == "Normal":
# print(fea_list[j])
pm_list.append(pm.Normal(str(fea_list[i])))
i += 1
elif type != "Target" and prior == "Student T":
pm_list.append(pm.StudentT(str(fea_list[i]), nu=nu))
i += 1
elif type != "Target" and prior == "Skew Normal":
pm_list.append(pm.SkewNormal(str(fea_list[i])))
i += 1
# for i, item in enumerate(prior_distribution_list):Skew Normal
# setattr(sys.modules[__name__], 'beta{0}'.format(i), item)
# hyper_sigma = pm.HalfNormal('hyper_sigma', sd=3)
sigma = pm.HalfCauchy('sigma', beta=10, testval=1.)
intercept = pm.Normal('Intercept', 0, sigma=20)
# setting the distribution mean for the predictor
mu = intercept
print("MUUU")
print(mu)
for i in range(len(pm_list)):
print("PM LIST i")
print(pm_list[i])
print("DF[FEA_LIST]")
print(df[fea_list[i]])
mu += pm_list[i] * df[fea_list[i]].to_numpy()
#mu += pm_list[i] * np.ones(df[fea_list[i]].to_list())
print(df[fea_list[i]].to_numpy())
for prior_dist in prior_distribution_list:
for type,prior in prior_dist.items():
if type == "Target" and prior == "Normal":
print("Target Normal")
likelihood = pm.Normal(str(target_col), mu=mu, sigma=sigma, observed=df[target_col])
elif type == "Target" and prior == "Student T":
print("Target Student")
likelihood = pm.StudentT(str(target_col), nu = nu , mu=mu, sd=sigma, shape = n_individuals)
elif type == "Target" and prior == "Skew Normal":
print("Target Skew")
mu = pm.Uniform('lambda_bl', 0., draw_sample)
likelihood = pm.SkewNormal(str(target_col),mu=mu, sigma=sigma,tau=None, alpha=1, sd=3)
elif type == "Target" and prior == nan:
print("Target Nan")
likelihood = pm.Normal(str(target_col), mu=mu, sigma=sigma, observed=df[target_col])
#means = pm.StudentT('means', nu = nu, mu = hyper_mean, sd = hyper_sigma, shape = n_individuals)
#SkewNormal(mu=0.0, sigma=None, tau=None, alpha=1, sd=None, *args, **kwargs)
trace = pm.sample(draws=draw_sample, chains=chains, random_seed=23,progressbar=True)
img_source = save_mat_fig(trace, gtype='traceplot')
posterior_dist = save_mat_fig(trace, gtype='posterior')
# return np.array([np.mean(trace[variable]) for variable in trace.varnames]), img_source, posterior_dist
return trace, img_source, posterior_dist
def preprocess_data(X, scaler=None):
if not scaler:
scaler = StandardScaler()
scaler.fit(X)
X = scaler.transform(X)
return X, scaler
# Format the features and labels for use with scikit learn
feature_list = []
label_list = []
for item in training_set:
if np.isnan(item[0]).sum() < 1:
feature_list.append(item[0])
label_list.append(item[1])
print('Features in Training Set: {}'.format(len(training_set)))
print('Invalid Features in Training set: {}'.format(
len(training_set) - len(feature_list)))
X = np.array(feature_list)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
X_train = X_scaler.transform(X)
y_train = np.array(label_list)
# Convert label strings to numerical encoding
encoder = LabelEncoder()
y_train = encoder.fit_transform(y_train)
# Create classifier
clf = svm.SVC(kernel='linear')
# Set up 5-fold cross-validation
kf = cross_validation.KFold(len(X_train),
n_folds=5,
shuffle=True,
plt.style.use('seaborn-deep')
import matplotlib.cm
cmap = matplotlib.cm.get_cmap('plasma')
# Reading in data
ds = pd.read_csv("Social_Network_Ads.csv")
X = ds.iloc[:, 2:4].values
y = ds.iloc[:, 4].values
# Splitting and scaling
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
X_train, X_test, y_train, y_test = train_test_split(X, y)
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
# Classifier
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
clf = SVC(kernel='rbf', C=20)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
accuracy = (cm[0][0] + cm[1][1]) / sum(sum(cm))
# Cross validation
def __init__(self, columns=None, **kwargs):
self.columns = columns
self.model = StandardScaler(**kwargs)
self.transform_cols = None
import pandas as pd
import plotly.figure_factory as ff
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.cluster import AgglomerativeClustering
import hvplot.pandas
# %%
df = pd.read_csv('new_iris_data.csv')
# %%
# Standardized first
iris_scale = StandardScaler().fit_transform(df)
# apply PCA to reduce to 2 pricipal components
pca_model = PCA(n_components=2, random_state=0)
iris_pca = pca_model.fit_transform(iris_scale)
print(pca_model.explained_variance_ratio_)
# %%
iris_pca_df = pd.DataFrame(data=iris_pca, columns=['PC_1', 'PC_2'])
iris_pca_df.head(-5)
# %%
# creating a dendrogram using plotly.figure_factory
fig = ff.create_dendrogram(iris_pca_df, color_threshold=0)
fig.update_layout(width=800, height=500)
fig.show()
# the higher the horizontal lines, the less similarity there is between the clusters.