def remove_and_correct_outliers(data):
##is data in a normal distribution??
b_constant = 1.4826 ##constant used for normal distribution
factor = 10 #3 ##factor to multiply for the range
count = 0
for i in range(0, len(data[0].values)): ##iterate through all features, in voce case 6125
d_s, d_ns, _, _ = utils.get_utterance_values_of_ith_utterance(data, i) ##get all feature values
d = d_s + d_ns ##join them together, since the fucntion returns different arrays for stress or not stress
f_vals = np.array(d, dtype=float) ##transform list into np array
median = np.median(f_vals) ##get the median
diff = (f_vals - median)**2 ##subtract median to every element and **2 to get all values to positive
diff = np.sqrt(diff) ## eliminate the **2 trick to avoid negatives
med_abs_deviation = np.median(diff) ##get the new mean
threshold = med_abs_deviation * b_constant ##raange of value to be accepted
max_range = median + threshold * factor
min_range = median - threshold * factor
for j in range(0, len(f_vals)): ##mark values that are outside the bounderies as outliers
if f_vals[j] < min_range or f_vals[j] > max_range:
count += 1
f_vals[j] = np.nan
imp = Imputer(missing_values=np.nan, strategy='mean', axis=1)
f_vals = imp.fit_transform(f_vals)[0]
for j in range(0, len(f_vals)):
data[j].values[i] = round(f_vals[j],6)
print "Detected ", count, " outliers"
return data
def get_modelKmeans():
# Connect to a pre-existing cluster
# connect to localhost:54321
# Log.info("Importing benign.csv data...\n")
benign_h2o = h2o.import_file(path=pyunit_utils.locate("smalldata/logreg/benign.csv"))
# benign_h2o.summary()
benign_sci = np.genfromtxt(pyunit_utils.locate("smalldata/logreg/benign.csv"), delimiter=",")
# Impute missing values with column mean
imp = Imputer(missing_values="NaN", strategy="mean", axis=0)
benign_sci = imp.fit_transform(benign_sci)
for i in range(2, 7):
# Log.info("H2O K-Means")
km_h2o = H2OKMeansEstimator(k=i)
km_h2o.train(x=range(benign_h2o.ncol), training_frame=benign_h2o)
km_h2o.show()
model = h2o.get_model(km_h2o._id)
model.show()
km_sci = KMeans(n_clusters=i, init="k-means++", n_init=1)
km_sci.fit(benign_sci)
print "sckit centers"
print km_sci.cluster_centers_
def get_features(frame):
'''
Transforms and scales the input data and returns a numpy array that
is suitable for use with scikit-learn.
Note that in unsupervised learning there are no labels.
'''
# Replace missing values with 0.0
# or we can use scikit-learn to calculate missing values below
#frame[frame.isnull()] = 0.0
# Convert values to floats
arr = np.array(frame, dtype=np.float)
# Impute missing values from the mean of their entire column
from sklearn.preprocessing import Imputer
imputer = Imputer(strategy='mean')
arr = imputer.fit_transform(arr)
# Normalize the entire data set to mean=0.0 and variance=1.0
from sklearn.preprocessing import scale
arr = scale(arr)
return arr
def data_organizer( instances, outcomes ):
"""
Operations to organize data as desired
"""
# Remove instances without GPA data
new_instances = []
new_outcomes = []
for instance,outcome in zip(instances,outcomes):
u1,u2,gpa = outcome
if not math.isnan( gpa ):
new_instances.append( [value for value in instance] )
new_outcomes.append( [value for value in outcome] )
instances = new_instances
outcomes = new_outcomes
# Fill in NaN values with median
instance_list = []
for idx,instance in enumerate(instances):
instance_list.append( [ value for value in instance ] )
bandaid = Imputer( strategy='median' )
instances = bandaid.fit_transform( instance_list )
return instances, outcomes
def run_whole_video(exp_folder, lims_ID):
#initializes video pointer for video of interest based on lims ID
file_string = get_file_string(exp_folder, lims_ID)
video_pointer = cv2.VideoCapture(file_string)
# import wheel data
wheel = joblib.load('dxds2.pkl')
first_non_nan = next(x for x in wheel if not isnan(x))
first_index = np.where(wheel == first_non_nan)[0]
k = first_index[0]
imp = Imputer(missing_values='NaN', strategy='mean')
wheel = imp.fit_transform(wheel)
wheel = preprocessing.MinMaxScaler((-1, 1)).fit(wheel).transform(wheel)
# self.video_pointer.set(1, 41000)
ret, frame = video_pointer.read()
# crops and converts frame into desired format
frame = cv2.cvtColor(frame[160:400, 100:640], cv2.COLOR_BGR2GRAY)
prvs = frame
nex = frame
# initialize vectors to keep track of data
count = 0
mod = 0
opticals = []
angles = []
frames = []
# length of movie
limit = int(video_pointer.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
# create hdf file
hf = h5py.File('data_' + str(lims_ID) + '.h5', 'w')
g = hf.create_group('feature space')
vector = np.zeros((limit, 4321))
table = g.create_dataset('features', data = vector, shape =(limit, 4321))
while count <= limit:
prvs = nex
frames = process_input(prvs)
ret, frame = video_pointer.read()
nex = cv2.cvtColor(frame[160:400, 100:640], cv2.COLOR_BGR2GRAY)
optical = optical_flow(prvs, nex)
opticals = optical['mag']
angles= optical['ang']
vector_data = np.concatenate((np.reshape(wheel[k], (1)), frames, opticals, angles))
table[count, :] = vector_data
count += 1
if count%1000 == 0:
print (count)
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
def impute_and_scale(df, scaling='std'):
"""Impute missing values with mean and scale data included in pandas dataframe.
Parameters
----------
df : pandas dataframe
dataframe to impute and scale
scaling : 'maxabs' [-1,1], 'minmax' [0,1], 'std', or None, optional (default 'std')
type of scaling to apply
"""
df = df.dropna(axis=1, how='all')
imputer = Imputer(strategy='mean', axis=0)
mat = imputer.fit_transform(df)
if scaling is None or scaling.lower() == 'none':
return pd.DataFrame(mat, columns=df.columns)
if scaling == 'maxabs':
scaler = MaxAbsScaler()
elif scaling == 'minmax':
scaler = MinMaxScaler()
else:
scaler = StandardScaler()
mat = scaler.fit_transform(mat)
df = pd.DataFrame(mat, columns=df.columns)
return df
def benignKmeans():
# Connect to a pre-existing cluster
# connect to localhost:54321
# Log.info("Importing benign.csv data...\n")
benign_h2o = h2o.import_file(path=pyunit_utils.locate("smalldata/logreg/benign.csv"))
#benign_h2o.summary()
benign_sci = np.genfromtxt(pyunit_utils.locate("smalldata/logreg/benign.csv"), delimiter=",")
# Impute missing values with column mean
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
benign_sci = imp.fit_transform(benign_sci)
# Log.info(paste("H2O K-Means with ", i, " clusters:\n", sep = ""))
from h2o.estimators.kmeans import H2OKMeansEstimator
for i in range(1,7):
benign_h2o_km = H2OKMeansEstimator(k=i)
benign_h2o_km.train(x = range(benign_h2o.ncol), training_frame=benign_h2o)
print "H2O centers"
print benign_h2o_km.centers()
benign_sci_km = KMeans(n_clusters=i, init='k-means++', n_init=1)
benign_sci_km.fit(benign_sci)
print "sckit centers"
print benign_sci_km.cluster_centers_
def learn(): global classifier, INPUT print 1 data = np.genfromtxt(INPUT, delimiter=' ', dtype='f8') np.random.shuffle(data) n = len(data) y = data[:,1] x = data[:][:,range(2,54)] # test_x = [] # test_y = [] train_x = [] train_y = [] print 2 imp = Imputer(missing_values='NaN', strategy='mean', axis=0) x = imp.fit_transform(x) print 3 for i in range(0, n): if y[i] == 0: continue train_x.append(x[i]) train_y.append(y[i]) # if i%100==0: # test_x.append(x[i]) # test_y.append(y[i]) # else: # train_x.append(x[i]) # train_y.append(y[i]) print 4 classifier.fit(train_x, train_y) print 5
def fit(self, train_x, train_y=None, is_norm=True):
# Normalization
if is_norm:
train_x_min = train_x.min(0)
train_x_ptp = train_x.ptp(axis=0)
train_x = train_x.astype(float) - train_x_min / train_x_ptp
if np.any(train_y):
train_y = train_y.astype(float) - train_x_min / train_x_ptp
imp = Imputer(missing_values='NaN', strategy='mean', axis=1)
imp.fit(train_x)
if np.isnan(train_x).any():
log("Found {} NaN values in train_x, so try to transform them to 'mean'".format(np.isnan(train_x).sum()), WARN)
train_x = imp.transform(train_x)
if np.any(train_y) and np.isnan(train_y).any():
log("Found {} NaN values in train_y, so try to transform them to 'mean'".format(np.isnan(train_y).sum()), WARN)
train_y = imp.transform(train_y)
if np.any(train_y):
self.model.fit(train_x, train_y)
else:
self.model.fit(train_x)
def preprocess(data):
non_sparse_only = True
use_all_category_only = False
use_all_impute_mean_mode = False
if non_sparse_only:
nominal_samples = data.ix[:,['var4','dummy']]
onehot_samples = onehot.transform(nominal_samples,['var4','dummy'])
onehot_samples = pd.DataFrame(onehot_samples.toarray())
numbered_samples = data.ix[:,['var7','var8','var10','var11','var13','var15','var17']]
numbered_samples[['var7','var8']] = numbered_samples[['var7','var8']].convert_objects(convert_numeric=True)
#(var7 and 8 are ordinal, converting to floats which includes NaNs will allow mean imputing of missing values)
other_samples = data.ix[:,'crimeVar1':'weatherVar236'] #all the continuous vars
other_samples = other_samples.drop(['weatherVar115'], axis=1) #nothing in this feature
samples = pd.concat([onehot_samples,numbered_samples,other_samples],axis=1) #combine w/ the cleaned up other vars
imp_nan = Imputer(missing_values=np.nan, strategy='mean', axis=0)
samples_imp = imp_nan.fit_transform(samples)
if use_all_category_only:
todo
if use_all_impute_mean_mode:
todo
return samples_imp
def run_main(new_file, start, stop, dat):
with open(new_file, 'a') as file:
imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=1)
import itertools
with open(dat, "r") as text_file:
for line in itertools.islice(text_file, start, stop):
line = line.replace("NA", "NaN")
content = line.rstrip('\n').split('\t')
CpG = content.pop(0)
flag, CpG_location = get_location(CpG)
if flag == 'F':
continue
genotype_matrix = get_genotypes(CpG_location)
genotype_matrix = imp.transform(genotype_matrix)
genotype_matrix = genotype_matrix.transpose()
#run PCA
try:
PCA_matrix = run_pca(genotype_matrix)
except ValueError:
print "value error"
continue
#run linear regression
meth_values = pd.Series(content, name="meth_val", dtype=float)
model = sm.OLS(meth_values, PCA_matrix)
results = model.fit()
MethValResids = results.resid
final = pd.Series(CpG)
final = final.append(MethValResids)
fline = final.tolist()
fline = '\t'.join(str(x) for x in fline)
fline = fline + "\n"
file.write(fline)
def test_3_stage(self):
from sklearn.preprocessing import Imputer
infile_name = path_of_data('missing_vals.csv')
p = Pipeline()
csv_read_node = p.add(CSVRead(infile_name))
csv_write_node = p.add(CSVWrite(self._tmp_files.get('out.csv')))
impute_node = p.add(wrap_and_make_instance(Imputer))
csv_read_node['output'] > impute_node['X_train']
impute_node['X_new'] > csv_write_node['input']
self.run_pipeline(p)
ctrl_imputer = Imputer()
ctrl_X_sa = np.genfromtxt(infile_name, dtype=None, delimiter=",",
names=True)
num_type = ctrl_X_sa[0][0].dtype
ctrl_X_nd, ctrl_X_sa_type = np_sa_to_nd(ctrl_X_sa)
ctrl_X_new_nd = ctrl_imputer.fit_transform(ctrl_X_nd)
control = ctrl_X_new_nd
result = self._tmp_files.csv_read('out.csv', True)
self.assertTrue(np.allclose(result, control))
def imputed_data(df, colname, strategy="mean"):
from sklearn.preprocessing import Imputer
imr = Imputer(missing_values="NaN", strategy=strategy, axis=0)
imr = imr.fit(df[colname].reshape(-1,1))
imputed_data = imr.transform(df[colname].values.reshape(-1,1))
df[colname] = imputed_data
print("Data has been imputed to \"{}\"".format(colname))
class ImputeCategorical(BaseEstimator, TransformerMixin):
"""
Encodes a specified list of columns or all columns if None.
"""
def __init__(self, columns=None):
self.columns = columns
self.imputer = None
def fit(self, data, target=None):
"""
Expects a data frame with named columns to impute.
"""
# Encode all columns if columns is None
if self.columns is None:
self.columns = data.columns
# Fit an imputer for each column in the data frame
self.imputer = Imputer(missing_values=0, strategy='most_frequent')
self.imputer.fit(data[self.columns])
return self
def transform(self, data):
"""
Uses the encoders to transform a data frame.
"""
output = data.copy()
output[self.columns] = self.imputer.transform(output[self.columns])
return output
def impute_missing_data(datapoints, strategy='mean'):
""" Inputes values for the 8 features missing data
Arguments:
datapoints -- X, a dataset with missing values represented 999.0 and 9999.0
strategy [optional] -- an imputation strategy,
e.g., mean, median, or most_frequent
Returns:
X_imputed -- a dataset with missing values imputed according to the
provided or default (mean) strategy.
Uses the scikit-learn Imputer class.
"""
# First we will replace our placeholder values with NaN to only have
# to run one imputation.
np.putmask(datapoints, datapoints == 999.0, np.NaN)
np.putmask(datapoints, datapoints == 9999.0, np.NaN)
# Now create an imputer over NaN values, and average over axis=0 (columns)
# Then, fit the imputer to the dataset.
imp = Imputer(strategy=strategy, axis=0)
X_imputed = imp.fit_transform(datapoints)
return X_imputed
def load_datasets(feature_paths, label_paths):
'''
读取特征文件和标签文件并返回
'''
#定义feature数组变量，列数量和特征维度一致为41；定义空的标签变量，列数量与标签维度一致为1
feature = np.ndarray(shape=(0,41))
label = np.ndarray(shape=(0,1))
for file in feature_paths:
#使用pandas库的read_table函数读取一个特征文件的内容，其中指定分隔符为逗号、缺失值为问号且文件不包含表头行
#df = pd.read_table(file, delimiter=',', na_values='?', header=None)
#pandas.read_csv（数据源, encoding=编码格式为utf-8， parse_dates=第0列解析为日期， index_col=用作行索引的列编号）
data=pd.read_csv(file,encoding='utf-8',parse_dates=[0],index_col=0)
#DataFrame.sort_index(axis=0 (按0列排), ascending=True（升序）, inplace=False（排序后是否覆盖原数据））
#data 按照时间升序排列
#data.sort_index(0,ascending=True,inplace=True)
#使用Imputer函数，通过设定strategy参数为‘mean’，使用平均值对缺失数据进行补全。
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
#fit()函数用于训练预处理器，transform()函数用于生成预处理结果。
imp.fit(df)
df = imp.transform(df)
#将预处理后的数据加入feature，依次遍历完所有特征文件
feature = np.concatenate((feature, df))
#读取标签文件
for file in label_paths:
df = pd.read_table(file, header=None)
label = np.concatenate((label, df))
#将标签归整化为一维向量
label = np.ravel(label)
return feature, label
def impute_missing_train(dataframe, missing_values='NaN', strategy='mean'):
'''
Given a dataframe, imputes missing values with a given strategy.
Supported strategies: 'mean', 'median', 'most_frequent'.
Returns dictionary mapping transformed columns to its imputer value.
'''
from sklearn.preprocessing import Imputer
imp = Imputer(missing_values=missing_values, strategy=strategy, axis=0)
imputed = imp.fit_transform(dataframe)
df = pd.DataFrame(imputed)
df.columns = list(dataframe.columns)
imputers = {}
if strategy == 'mean':
for col in df.columns:
mean = df[col].mean()
imputers[col] = mean
if strategy == 'median':
for col in df.columns:
median = df[col].median()
imputers[col] = median
if strategy == 'most_frequent':
for col in df.columns:
mode = df[col].mode()
imputers[col] = mode
return df, imputers
def Train_And_Test(self):
HOG_data=np.loadtxt('dataset.csv',delimiter=",")
tmpdata=HOG_data[:,0:-2]
target=HOG_data[:,-2]
print(target)
tmpdata[tmpdata==0]=np.nan
imp=Imputer(missing_values='NaN',strategy='mean')
data=imp.fit_transform(tmpdata)
data_train,data_test,target_train,target_test=train_test_split(data,target,test_size=0.3)
model=SVC(C=1.0,gamma=0.0,kernel='linear', class_weight='auto')
model.fit(data_train,target_train)
print(data_train)
print(target_train)
opencv_data_train=np.float32(data_train)
opencv_target_train=np.float32(target_train)
svm_params = dict( kernel_type = cv2.SVM_LINEAR,
svm_type = cv2.SVM_C_SVC,
C=2.67, gamma=5.383)
svm = cv2.SVM()
svm.train(opencv_data_train,opencv_target_train, params=svm_params)
svm.save("hog_classifier.xml")
print(model)
expected=target_test
predicted=model.predict(data_test)
target_names = ['Not Human', 'Human']
print(metrics.classification_report(expected,predicted,target_names=target_names))
print(metrics.confusion_matrix(expected,predicted))
print(metrics.roc_curve(expected,predicted))
pickle.dump(model, open( "svm.p", "wb" ) )
def avg_message_count_by_group(df_users, df_messages, df_user_features):
columns = ["f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9", "f10"]
features = df_user_features[list(columns)].values
# Impute missing values to retain all sample data
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
X = imp.fit_transform(features)
# Preprocess dataset and standardize features to have normally distributed data
# MaxAbsScaler allows scaled features to lie between -1 and +1
X = MaxAbsScaler().fit_transform(X)
# Apply PCA decomposition and use first 3 components that explain 75% of variance
reduced_data = decomposition.PCA(n_components=3).fit_transform(X)
kmeans = KMeans(init='k-means++', n_clusters=5, n_init=10)
# Predict which group each user belongs to
cluster_labels = kmeans.fit_predict(reduced_data)
df_user_features['group.id'] = cluster_labels
# Call utility function to join the two dataframes
df_joined_users_messages = get_merged_dataframes(df_users, df_messages)
df_joined_users_messages_features = get_merged_dataframes(df_user_features, df_joined_users_messages)
# Only keep messages that were received since signing up
df_joined_users_messages_features = df_joined_users_messages_features[df_joined_users_messages_features['message.date']
>= df_joined_users_messages_features['signup.date']]
# Get the average message count grouped by group.id
avg_message_count = df_joined_users_messages_features.groupby('group.id')['message.count'].mean()
# Return the average message count grouped by user groups and rounded to 2 decimals
return np.round(avg_message_count.tolist(), decimals=2)
def fill_and_remove(self, s_strategy="zeros", l_features = False,
b_remove = True):
'''
fill all Nan values in numerical data with zeros and then remove data
points that all features are equal to zero
l_features: a list of features to be tested. If any, all features will
be used
b_remove: boolean indicating if should remove keys where all data is 0
s_strategy: string with the strategy used to fill NaNs. Can be "mean",
"median" and "zeros"
'''
df = self.getData()
#pre-process data
if not l_features:
l_features = self.payments_features + self.stock_features
l_features+= self.email_features
df.loc[:, l_features] = df.loc[:, l_features].astype(float)
#filling Nan with the strategy selected
if s_strategy == "zeros":
df.loc[:, l_features] = df.loc[:, l_features].fillna(0)
else:
na_X = df.loc[:, l_features].values
imp = Imputer(missing_values='NaN', strategy=s_strategy, axis=0)
df.loc[:, l_features] = imp.fit_transform(na_X)
#exclude datapoint where every number is equal to 0
if b_remove:
df = df.ix[((df.loc[:, l_features]!=0).sum(axis=1)!=0),:]
#saving the new dataframe
self.setData(df)
#correct scaled df
if type(self.df_scaled)!=list:
df2 = self.df_scaled
df2 = df2.ix[((df.loc[:, l_features]!=0).sum(axis=1)!=0).index,:]
self.df_scaled = df2
def run_importance(clf, data, labels, feature_labels=[""], string=""):
"""
Fit a classifier using all the data and plot the feature importances
:param clf: Classifier object that has feature_importances_ member
:param feature_labels: names of the features
:param string: classifier name
:return: (void) plot Gini importance vs feature
"""
num_features = data.shape[1]
importances = [0]*num_features
imp = Imputer(missing_values=np.NaN, strategy="mean")
data = imp.fit_transform(data)
# run the classifier 100 times and average the importance found after each fit
for r in range(100):
clf.fit(data, labels)
importances = [importances[i]+clf.feature_importances_[i] for i in range(num_features)]
importances = [importance/100 for importance in importances]
# Filter out the features that have 0 importance (e.g. values are all 0)
# non_zeros are the indices in feature_importances that are not 0
non_zeros = [i for i in range(num_features) if not importances[i] == 0]
importances = [importances[i] for i in non_zeros]
feature_labels = [feature_labels[i] for i in non_zeros]
# Plot the features
bar_width = 0.7
plt.bar(range(len(feature_labels)), importances, bar_width)
plt.xticks([ind + +float(bar_width)/2 for ind in range(len(feature_labels))], feature_labels,rotation="vertical")
plt.gcf().subplots_adjust(bottom=0.35)
plt.xlabel("Feature")
plt.ylabel("Gini Importance")
plt.title("Gini Importance v. Features for "+string+" Classifier")
plt.show()
def test():
vec = DictVectorizer()
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
for filename in glob.glob(r'../dataset/UCI/*.arff'):
basename = re.sub(r'(\..*?)$','',os.path.basename(filename))
print basename
if basename != DS:
continue
# cost_matrix = pickle.load(open('../dataset/UCI/'+basename+'_cost_matrix.pkl', 'rb'))
data = arff.loadarff(filename)[0]
X = vec.fit_transform(np.array([{str(i):value for i,value in enumerate(list(row)[:-1])} for row in data])).toarray()
imp.fit(X)
X = imp.transform(X)
labels = np.array([row[-1] for row in data])
y = np.array([{v:k for k,v in enumerate(list(set(labels)))}[label] for label in labels])
random = np.random.permutation(range(len(X)))
print 'dataset ratio\t%s'%('\t'.join([alg+" "*(12-len(alg)) for alg in sorted(ALG.keys())]))
for iteration in xrange(10):
X, y, class_num, kf = X[random], y[random], set(labels), KFold(len(X), n_folds=10)
for train, test in kf:
length, train_size = len(train), 0.1
X_train, X_test, y_train, y_test = X[train], X[test], y[train], y[test]
X_label, X_unlabel, y_label, y_unlabel = train_test_split(X_train, y_train, test_size=1.0-train_size, random_state=0)
for R in xrange(2,10):
ones_matrix, cost_matrix = np.array([[1,1],[1,1]]), np.array([[1,1],[R,R]])
# print "%s R=%d"%(basename,R),
cross_validation("%s R=%d"%(basename,R), X_label, X_unlabel, y_label, y_unlabel, ones_matrix, cost_matrix)
exit()
def computePearson(args):
filter(args)
with open(args.feature_file, 'r') as fp:
features = [line for line in fp.read().splitlines()
if not line.startswith('#')]
X = loadtxt(TMP_DATA_FILE)
y = loadtxt(TMP_LABEL_FILE)
assert X.shape[0] == y.shape[0]
assert X.shape[1] == len(features)
imputer = Imputer(strategy='median', copy=False)
X = imputer.fit_transform(X)
if args.output_file:
with open(args.output_file, 'w') as fp:
print >> fp, '\t'.join(['feature', 'coeff', 'pvalue'])
for i in range(len(features)):
coeff, pvalue = pearsonr(X[:, i], y)
print >> fp, '%s\t%f\t%f' % (features[i], coeff, pvalue)
if args.group_output_file:
groups = getGroups(features)
index = {features[i]: i for i in range(len(features))}
with open(args.group_output_file, 'w') as fp:
print >> fp, '\t'.join(['prefix', 'feature1', 'feature2', 'coeff', 'pvalue'])
for prefix, group in groups.iteritems():
for i in range(len(group)):
for j in range(i+1, len(group)):
coeff, pvalue = pearsonr(X[:, index[group[i]]], X[:, index[group[j]]])
print >> fp, '%s\t%s\t%s\t%f\t%f' % (
prefix, group[i], group[j], coeff, pvalue)
def gettestdata(fil) : data = np.genfromtxt(fil,delimiter=',') imp = Imputer(missing_values='NaN', strategy='median', axis=0) X = imp.fit_transform(data[:,2:]) X = scale(X).copy() #spr.eliminate_zeros() return np.array(X)
def calcEdges(data):
n = len(data)
usersDic = {}
usersId = 0
moviesDic = {}
moviesId = 0
for i in range(n):
r = data[i]
if r[0] not in moviesDic:
moviesDic[r[0]] = moviesId
moviesId += 1
if r[1] not in usersDic:
usersDic[r[1]] = usersId
usersId += 1
E = np.zeros((moviesId, usersId))
#E = np.full((moviesId, usersId), np.nan)
for i in range(n):
user = usersDic[data[i][1]]
movie = moviesDic[data[i][0]]
E[movie, user] = data[i][2]
estimator = Imputer(0, strategy='mean')
#estimator = SoftImpute()
#estimator.fit(E)
#E = estimator.predict(E)
E = estimator.fit_transform(E)
return E, usersDic, moviesDic
def get_some_data():
data = melbourne_data;
y = data.Price
X = data[cols_to_use]
my_imputer = Imputer()
imputed_X = my_imputer.fit_transform(X)
return imputed_X, y
def plot_ROCList(clfList, data, labels, stringList=""):
"""
Plot an ROC curve for each classifier in clfList, training on a single 80/20 split
:param clfList:
:param data:
:param labels:
:param stringList:
:return:
"""
if stringList == "":
stringList = ["" for i in range(len(labels))]
imp = Imputer(missing_values=np.NaN, strategy="mean")
data = imp.fit_transform(data)
# Cross-validate on the data once using each model to get a ROC curve
AUCs, fprs, tprs, threshs = cvList(data, labels, clfList)
# Plote a ROC for each clf in clfList
for i in range(len(clfList)):
fpr = fprs[i]
tpr = tprs[i]
plt.plot(fpr, tpr)
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title(stringList[i]+" ROC Curve, AUC = "+str(AUCs[i]))
plt.savefig(stringList[i]+"_ROC.png")
plt.close()
print stringList[i] + ":" + str(AUCs[i])
def bnp_svm(train, test):
print('bnpsvm')
## If a value is missing, set it to the average
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
#print("cleaning data")
train = train.sample(1000)
## set up training data
train1 = train.select_dtypes(include=['float64'])
imp.fit(train1)
train1 = imp.transform(train1)
train1 = np.array(train1).astype(float)
## set up real y
target = np.array(train['target']).astype(int)
## set up testing data
test1 = test.select_dtypes(include=['float64'])
test1 = imp.transform(test1)
test1 = np.array(test1).astype(float)
#print("training...")
clf = svm.SVC(gamma=0.001, C=100, probability=True)
#print("testing")
clf.fit(train1, target)
#print("predicting")
yhat = clf.predict_proba(test1)
return yhat
#print(bnp_svm(train, test))
def run_clfList(clfList, stringList="", normalize=False):
"""
Run 100-fold 80/20 cross-validation on each classifier in clfList
print the average AUC for each classifier
:param clfList: list of classifiers to run
:param stringList: names of the classifiers
:param normalize: whether or not to normalize the data
:return: the average AUC for each classifier in clfList
"""
# data, labels = six_features(force=False)
# data, labels = six_and_time_features(force=False)
# data, labels = five_features(force=False)
# data, labels = five_and_rts(force=False)
data, labels = new_features()
if normalize:
data = normalize_data(data)
imp = Imputer(missing_values=np.NaN, strategy="mean")
data = imp.fit_transform(data)
# Cross-validate all clfs 100 times
means = kfoldcvList(data, labels, clfList, 100)
if stringList == "":
stringList = ["" for i in range(len(labels))]
# Print out the mean AUCs
for i, mean in enumerate(means):
print stringList[i]+": "+str(mean)
for mean in means:
sys.stdout.write(str(mean) + " & ")
sys.stdout.write("\n")
return means
# Importing the libraries
import numpy as np #contains mathematical tools
import matplotlib.pyplot as plt #plot charts
import pandas as pd #to import and manage datasets
# Importing dataset
dataset = pd.read_csv('Data.csv') #reading dataset
# iloc -> integer-location based indexing for selection by position.
X = dataset.iloc[:, :
-1].values #taking all columns except last one which is output label
Y = dataset.iloc[:, 3].values #taking column of output label
# Taking care of missing data
from sklearn.preprocessing import Imputer #for completing missing values .Select and Press Ctrl +I to see syntax
imputer = Imputer(missing_values='NaN', strategy='mean',
axis=0) #Replace missing values by mean
imputer = imputer.fit(X[:, 1:3]) #since index 1 and 2 contains missing columns
X[:, 1:3] = imputer.transform(X[:, 1:3])
#Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder #LabelEncoder to encode values and OneHotEncoder to give dummy values
labelEncoder_X = LabelEncoder()
X[:, 0] = labelEncoder_X.fit_transform(
X[:, 0]) #to categorize Country column gives encoded values
onehotencoder = OneHotEncoder(
categorical_features=[0]) # to give which column to categorize
X = onehotencoder.fit_transform(X).toarray()
#to categorize output label it wont need OneHotEncoder since it is dependent variable with only 2 labels Yes or No
labelEncoder_Y = LabelEncoder()
Y = labelEncoder_Y.fit_transform(
# Data Preprocessing Template
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 3].values
# filling the missing values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='median',
axis=0) # 0 for along columns and 1 for along rows
imputer.fit(X[:, 1:3]) # 3 is exclude (1 and 2 have missing data)
X[:, 1:3] = imputer.transform(X[:, 1:3]) # missing data will be filled
# Encoding categorical data (countries here)
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
# As we cant categories countries so we will create dummy variables(matrix form)
onehotencoder = OneHotEncoder(categorical_features=[0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y) # 0 to no and y to yes
def processData():
train = pd.read_csv("train.csv")
catFeatures = []
numFeatures = []
for name, val in zip(train.columns, train.dtypes):
if val in [np.dtype('O'), np.dtype('int64')]:
if name not in [
'GTIME', 'GSTATUS_THREE_MONTHS', 'GSTATUS_SIX_MONTHS',
'GSTATUS_ONE_YEAR', 'GSTATUS_THREE_YEARS'
]:
catFeatures.append(name)
else:
numFeatures.append(name)
# catFeatures = ['GENDER', 'ABO', 'LIFE_SUP_TCR', 'MALIG_TCR', 'EXC_HCC', 'EXC_CASE', 'PERM_STATE', 'PREV_AB_SURG_TCR', 'BACT_PERIT_TCR', 'PORTAL_VEIN_TCR', 'TIPSS_TCR', 'WORK_INCOME_TCR', 'INIT_DIALYSIS_PRIOR_WEEK', 'INIT_MELD_OR_PELD', 'FINAL_DIALYSIS_PRIOR_WEEK', 'FINAL_MELD_OR_PELD', 'PERM_STATE_TRR', 'WORK_INCOME_TRR', 'MALIG_TRR', 'LIFE_SUP_TRR', 'PORTAL_VEIN_TRR', 'PREV_AB_SURG_TRR', 'TIPSS_TRR', 'HBV_CORE', 'HBV_SUR_ANTIGEN', 'HCV_SEROSTATUS', 'EBV_SEROSTATUS', 'HIV_SEROSTATUS', 'CMV_STATUS', 'CMV_IGG', 'CMV_IGM', 'TXLIV', 'PREV_TX', 'DDAVP_DON', 'CMV_DON', 'HEP_C_ANTI_DON', 'HBV_CORE_DON', 'HBV_SUR_ANTIGEN_DON', 'DON_TY', 'GENDER_DON', 'HOME_STATE_DON', 'NON_HRT_DON', 'ANTIHYPE_DON', 'PT_DIURETICS_DON', 'PT_STEROIDS_DON', 'PT_T3_DON', 'PT_T4_DON', 'VASODIL_DON', 'VDRL_DON', 'CLIN_INFECT_DON', 'EXTRACRANIAL_CANCER_DON', 'HIST_CIG_DON', 'HIST_COCAINE_DON', 'DIABETES_DON', 'HIST_HYPERTENS_DON', 'HIST_OTH_DRUG_DON', 'ABO_DON', 'INTRACRANIAL_CANCER_DON', 'SKIN_CANCER_DON', 'HIST_CANCER_DON', 'PT_OTH_DON', 'HEPARIN_DON', 'ARGININE_DON', 'INSULIN_DON', 'DIAL_TX', 'ABO_MAT', 'AGE_GROUP', 'MALIG', 'RECOV_OUT_US', 'TATTOOS', 'LI_BIOPSY', 'PROTEIN_URINE', 'CARDARREST_NEURO', 'INOTROP_SUPPORT_DON', 'CDC_RISK_HIV_DON', 'HISTORY_MI_DON', 'CORONARY_ANGIO_DON', 'LT_ONE_WEEK_DON']
# numFeatures = ['WGT_KG_DON_CALC', 'INIT_INR', 'ETHCAT_DON', 'ETHNICITY', 'DGN_TCR', 'REM_CD', 'INIT_AGE', 'ALBUMIN_TX', 'BMI_DON_CALC', 'EXC_EVER', 'OTH_LIFE_SUP_TCR', 'FINAL_ASCITES', 'WGT_KG_CALC', 'END_BMI_CALC', 'LISTYR', 'DDR1', 'FINAL_ALBUMIN', 'DB2', 'INIT_BMI_CALC', 'CITIZENSHIP', 'DB1', 'EDUCATION', 'DAYSWAIT_CHRON', 'OTH_LIFE_SUP_TRR', 'MED_COND_TRR', 'INIT_WGT_KG', 'MELD_PELD_LAB_SCORE', 'NUM_PREV_TX', 'INIT_SERUM_SODIUM', 'VENTILATOR_TCR', 'TX_PROCEDUR_TY', 'LITYP', 'INIT_SERUM_CREAT', 'WGT_KG_TCR', 'TBILI_DON', 'HGT_CM_CALC', 'SGOT_DON', 'ASCITES_TX', 'INIT_MELD_PELD_LAB_SCORE', 'ECD_DONOR', 'CREAT_TX', 'INIT_ENCEPH', 'INIT_HGT_CM', 'PRI_PAYMENT_TRR', 'INIT_STAT', 'ARTIFICIAL_LI_TCR', 'PT_CODE', 'WL_ID_CODE', 'INIT_ALBUMIN', 'ARTIFICIAL_LI_TRR', 'AGE_DON', 'ON_VENT_TRR', 'PRI_PAYMENT_TCR', 'BLOOD_INF_DON', 'CREAT_DON', 'REGION', 'INIT_ASCITES', 'HEMATOCRIT_DON', 'DIAB', 'TBILI_TX', 'FINAL_INR', 'AGE', 'FUNC_STAT_TRR', 'ETHCAT', 'CITIZENSHIP_DON', 'DEATH_MECH_DON', 'FUNC_STAT_TCR', 'FINAL_SERUM_SODIUM', 'COD_CAD_DON', 'FINAL_BILIRUBIN', 'BUN_DON', 'END_STAT', 'BMI_CALC', 'DDR2', 'FINAL_SERUM_CREAT', 'HIST_DIABETES_DON', 'ENCEPH_TX', 'SHARE_TY', 'DA1', 'PH_DON', 'FINAL_MELD_PELD_LAB_SCORE', 'BMI_TCR', 'INIT_BILIRUBIN', 'DISTANCE', 'SGPT_DON', 'PULM_INF_DON', 'HGT_CM_TCR', 'TRANSFUS_TERM_DON', 'FINAL_ENCEPH', 'DIAG', 'DA2', 'HGT_CM_DON_CALC', 'URINE_INF_DON', 'COLD_ISCH', 'INR_TX', 'DEATH_CIRCUM_DON', 'CANCER_SITE_DON']
#Categorical pipeline
cat_pipeline = Pipeline([
('selector', DataFrameSelector(catFeatures)),
('imputer', CategoricalImputer()),
('cat_encoder',
CategoricalEncoder("onehot-dense", handle_unknown='ignore')),
])
#Numerical pipeline
num_pipeline = Pipeline([
('selector', DataFrameSelector(numFeatures)),
('imputer', Imputer(strategy="median")),
('std_scaler', StandardScaler()),
])
#Full pipeline
full_pipeline = FeatureUnion(transformer_list=[
("num_pipeline", num_pipeline),
("cat_pipeline", cat_pipeline),
])
# train = pd.read_csv("train.csv")
X_train = full_pipeline.fit_transform(train.loc[:,
catFeatures + numFeatures])
gstatusSixMonths_train = train["GSTATUS_SIX_MONTHS"].values
gstatusOneYear_train = train["GSTATUS_ONE_YEAR"].values
gstatusThreeYears_train = train["GSTATUS_THREE_YEARS"].values
gstatus_train = train["GSTATUS_THREE_YEARS"].values
gtime_train = train["GTIME"].values
Y_train = np.array([[gstatus_train[i], gtime_train[i]]
for i in range(len(gtime_train))
]) #[is_not_censored, survival time]
test = pd.read_csv("test.csv")
X_test = full_pipeline.transform(test.loc[:, catFeatures + numFeatures])
gstatusSixMonths_test = test["GSTATUS_SIX_MONTHS"].values
gstatusOneYear_test = test["GSTATUS_ONE_YEAR"].values
gstatusThreeYears_test = test["GSTATUS_THREE_YEARS"].values
gstatus_test = test["GSTATUS_THREE_YEARS"].values
gtime_test = test["GTIME"].values
Y_test = np.array([[gstatus_test[i], gtime_test[i]]
for i in range(len(gtime_test))
]) #[is_not_censored, survival time]
return X_train, Y_train, X_test, Y_test
import pandas as pd
base = pd.read_csv('./datasets/credit-data.csv') #load a file
base.loc[base.age < 0, 'age'] = base['age'][
base.age > 0].mean() #replace all negative ages to ages mean
base.loc[pd.isnull(base['age'])] #find out on base all indexes with null age
forecasts = base.iloc[:, 1:
4].values #forecasts variable will receive the columns 1,2 and 3 of base. The ":" means we want all lines.
classes = base.iloc[:, 4].values
#Using sklearn to localize all missing_values and replace using a strategy
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(forecasts[:, 1:4])
forecasts[:, 1:4] = imputer.transform(forecasts[:, 1:4])
#when using knn algorithms is necessary standardisation or normalization
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
forecasts = scaler.fit_transform(forecasts)
#Divide database in training data and test data
from sklearn.cross_validation import train_test_split
forecasts_training, forecasts_testing, classes_training, classes_testing = train_test_split(
forecasts, classes, test_size=0.25, random_state=0)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(forecasts_training, classes_training)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 18 20:07:04 2018
@author: diego
"""
import pandas as pd
import numpy as np
from sklearn.preprocessing import Imputer, MinMaxScaler
data = pd.read_csv('../data/pacientes_ucic.csv', sep=';')
imputer = Imputer()
minmaxscaler = MinMaxScaler()
data['SAPS-3'] = imputer.fit_transform(data[['SAPS-3']])
"""
data['SAPS-3'] = minmaxscaler.fit_transform(data[['SAPS-3']])
"""
print(data['SAPS-3'])
#print(corr_matrix["median_house_value"].sort_values(ascending=False))
housing = strat_train_set.drop("median_house_value", axis=1)
housing_labels = strat_train_set["median_house_value"].copy()
sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()
#print(sample_incomplete_rows)
sample_incomplete_rows.dropna(subset=["total_bedrooms"])
sample_incomplete_rows.drop("total_bedrooms", axis=1)
median = housing["total_bedrooms"].median()
sample_incomplete_rows["total_bedrooms"].fillna(median, inplace=True)
#print(sample_incomplete_rows)
imputer = Imputer(strategy="median")
housing_num = housing.drop('ocean_proximity', axis=1)
imputer.fit(housing_num)
#print(imputer.statistics_)
#print(housing_num.median().values)
X = imputer.transform(housing_num)
housing_tr = pd.DataFrame(X,
columns=housing_num.columns,
index=list(housing.index.values))
housing_tr.loc[sample_incomplete_rows.index.values]
import pandas as pd
dataset = pd.read_csv('data1.csv')
X = dataset.iloc[:, 1:11].values
y = dataset['Class']
'''
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[:, 1:11] = labelencoder.fit_transform(X[:,1:11])
onehotencoder = OneHotEncoder(categorical_features = [10])
X = onehotencoder.fit_transform(X).toarray()'''
# Handling missing values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(X[:, 1:11])
X[:, 1:11] = imputer.transform(X[:, 1:11])
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()
X_tr[:, 34] = labelencoder_X.fit_transform(X_tr[:, 34].astype(str))
X_tr[:, 35] = labelencoder_X.fit_transform(X_tr[:, 35].astype(str))
X_tr[:, 68] = labelencoder_X.fit_transform(X_tr[:, 68].astype(str))
X_tr[:, 93] = labelencoder_X.fit_transform(X_tr[:, 93].astype(str))
X_ts[:, 34] = labelencoder_X.fit_transform(X_ts[:, 34].astype(str))
X_ts[:, 35] = labelencoder_X.fit_transform(X_ts[:, 35].astype(str))
X_ts[:, 68] = labelencoder_X.fit_transform(X_ts[:, 68].astype(str))
X_ts[:, 93] = labelencoder_X.fit_transform(X_ts[:, 93].astype(str))
# missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(X_tr[:, :])
X_tr[:, :] = imputer.transform(X_tr[:, :])
imputer = imputer.fit(X_ts[:, :])
X_ts[:, :] = imputer.transform(X_ts[:, :])
# Encoding categorical data: OneHotEncoder
from sklearn.preprocessing import OneHotEncoder
a = np.concatenate((X_tr, X_ts))
onehotencoder = OneHotEncoder(categorical_features=[34, 35, 68, 93],
sparse=True)
a = onehotencoder.fit_transform(a).toarray()
X_tr = a[:len(X_tr), :]
X_ts = a[len(X_tr):, :]
import seaborn as sns
sns.boxplot(x="Pclass", y="Age", hue="Survived",data=dataset, palette="Set3")
'''
--------------------------------------------------------------------------------------------------
DATA PREP
--------------------------------------------------------------------------------------------------
'''
# SELECTING X and y
X = dataset.iloc[:,[3,4]].values
# X = dataset.iloc[:,[1,3]].values
# HANDLING MISSING DATA
# fillnan with mean/median/most_frequent
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(X.iloc[:,0].values.reshape((len(X),1)))
X.iloc[:,0] = imputer.transform(X.iloc[:,0].values.reshape((len(X),1)))
X.info()
X.drop(0, axis=1, inplace=True)
# Drop Nan
X.dropna(inplace = True)
y.dropa(inplace = True)
# ENCODING CATEGORICAL FEATURES
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
# The values 0,1,2,etc into categorical values
labelencoder_X = LabelEncoder()
X.iloc[:,1] = labelencoder_X.fit_transform(X.iloc[:, 1])
# Here we create the dummies
onehotencoder = OneHotEncoder(categorical_features=[1])
import pandas as pd
from sklearn import model_selection
import pickle
# Importing the dataset for training
dataset = pd.read_csv('train.csv')
new_data = dataset.iloc[:, [0, 1, 2, 4, 5, 6, 7, 9, 11]]
##encoding the training data
data_dummy = pd.get_dummies(new_data)
##
X_train = data_dummy.iloc[:, [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]].values
y_train = data_dummy.iloc[:, 1].values
#missing valuse addressing for training data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(X_train[:, [2]])
X_train[:, [2]] = imputer.transform(X_train[:, [2]])
np.set_printoptions(threshold=np.nan)
test_dataset = pd.read_csv('test.csv')
test_verify = pd.read_csv('gender_submission.csv')
new_test_data = test_dataset.iloc[:, [0, 1, 3, 4, 5, 6, 8, 10]]
##encoding the training data
test_data_dummy = pd.get_dummies(new_test_data)
##
X_test = test_data_dummy.iloc[:, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]].values
y_test = test_verify.iloc[:, [1]].values
#missing valuse addressing for training data
from sklearn.preprocessing import Imputer
#test_X = test_data.drop(['Id'], axis=1)
low_cardinality_cols = [
cname for cname in train_X.columns
if train_X[cname].nunique() < 10 and train_X[cname].dtype == "object"
]
numeric_cols = [
cname for cname in train_X.columns
if train_X[cname].dtype in ['int64', 'float64']
]
my_cols = low_cardinality_cols + numeric_cols
train_predictors = train_X[my_cols]
#test_predictors = test_X[my_cols]
#print(train_predictors.shape)
one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)
#one_hot_encoded_test_predictors = pd.get_dummies(test_predictors)
#print(*one_hot_encoded_training_predictors.columns, sep=',')
#my_submission = pd.DataFrame({'Id': test_data.Id, 'SalePrice': rf_val_predictions})
#one_hot_encoded_training_predictors.to_csv('TRAINING_1.csv', index=False)
#final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors,
# join='left',
# axis=1)
my_pipeline = make_pipeline(Imputer(), RandomForestRegressor())
#my_pipeline.fit(final_train, y)
#print(my_pipeline.predict(final_test))
scores = cross_val_score(my_pipeline,
one_hot_encoded_training_predictors,
y,
scoring='neg_mean_absolute_error')
print('Mean Absolute Error %2f' % (-1 * scores.mean()))
import pandas as pd
base = pd.read_csv('credit-data.csv')
base.loc[base.age < 0, 'age'] = 40.92
previsores = base.iloc[:, 1:4].values
classe = base.iloc[:, 4].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(previsores[:, 1:4])
previsores[:, 1:4] = imputer.transform(previsores[:, 1:4])
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
previsores = scaler.fit_transform(previsores)
from sklearn.model_selection import train_test_split
previsores_treinamento, previsores_teste, classe_treinamento, classe_teste = train_test_split(
previsores, classe, test_size=0.25, random_state=0)
from sklearn.neural_network import MLPClassifier
classificador = MLPClassifier(verbose=True,
max_iter=1000,
tol=0.0000010,
solver='adam',
hidden_layer_sizes=(100),
activation='relu')
classificador.fit(previsores_treinamento, classe_treinamento)
previsoes = classificador.predict(previsores_teste)
import numpy as np
# import data and labels using python array handling package numpy
data = np.loadtxt(
"/Users/071cht/Desktop/programming_language_tutorial/Python/scikit/Ye_thesis/data_scikit/data.txt",
delimiter=',')
labels = np.loadtxt(
"/Users/071cht/Desktop/programming_language_tutorial/Python/scikit/Ye_thesis/data_scikit/labels.txt"
)
intLabels = labels.astype(int)
# scikit has a imputer class which provide basic strategies for imputing missing values, using mean/median/or the most frequent values of the row or column
# in which the missing values are located.
#the following code retures an np.array, data_nonmissing. It is a non-missing value version of data
from sklearn.preprocessing import Imputer
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit(data)
data_nonmissing = imp.transform(data)
#rename data and intLables to chase_X and chase_y for model fitting convenience
X = data_nonmissing
y = intLabels
#set training and testing? data for 7-fold cross validation
seperateIdx = len(X) * 60 / 100
X_train = X[0:seperateIdx]
y_train = y[0:seperateIdx]
X_test = X[seperateIdx:]
y_test = y[seperateIdx:]
print X_train.shape, X_test.shape
# Data Preprocessing
# Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Data.csv')
x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 3].values
# Take care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
imputer = imputer.fit(x[:, 1:3])
x[:, 1:3] = imputer.transform(x[:, 1:3])
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_x = LabelEncoder()
x[:, 0] = labelencoder_x.fit_transform(x[:, 0])
onehotencoder = OneHotEncoder(categorical_features=[0])
x = onehotencoder.fit_transform(x).toarray()
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
if(np.isnan(df_test['Age'][i])):
if(df_test['Title'][i] == 'Mr'):
df_test['Age'][i] = 25
if(df_test['Title'][i] == 'Mrs'):
df_test['Age'][i] = 25
if(df_test['Title'][i] == 'Miss'):
df_test['Age'][i] = 5
if(df_test['Title'][i] == 'Master'):
df_test['Age'][i] = 5
# In[ ]:
#After removing these features, it's time to fill the missing values
imputer = Imputer(missing_values = np.nan, strategy = 'median', axis = 0)
df_train[['Age']] = imputer.fit_transform(df_train[['Age']])
df_test[['Age']] = imputer.fit_transform(df_test[['Age']])
df_train.loc[ df_train['Age'] <= 16, 'Age'] = 0
df_train.loc[(df_train['Age'] > 16) & (df_train['Age'] <= 32), 'Age'] = 1
df_train.loc[(df_train['Age'] > 32) & (df_train['Age'] <= 48), 'Age'] = 2
df_train.loc[(df_train['Age'] > 48) & (df_train['Age'] <= 64), 'Age'] = 3
df_train.loc[ df_train['Age'] > 64, 'Age'] = 4
df_test.loc[ df_test['Age'] <= 16, 'Age'] = 0
df_test.loc[(df_test['Age'] > 16) & (df_test['Age'] <= 32), 'Age'] = 1
df_test.loc[(df_test['Age'] > 32) & (df_test['Age'] <= 48), 'Age'] = 2
df_test.loc[(df_test['Age'] > 48) & (df_test['Age'] <= 64), 'Age'] = 3
df_test.loc[ df_test['Age'] > 64, 'Age'] = 4
#coding:utf8
import numpy as np
import pandas as pd
from sklearn.preprocessing import Imputer, LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
df = pd.read_csv("./resource/1.csv")
X = df.iloc[:, :-1].values
Y = df.iloc[:, -1].values
# dealing with missing data
imputer = Imputer(missing_values="NaN", strategy="mean")
imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])
# print(X)
# deal with class y data
encoder_X = LabelEncoder()
X[:, 0] = encoder_X.fit_transform(X[:, 0])
# print(X)
one_hot_encoder = OneHotEncoder(categorical_features=[0])
X = one_hot_encoder.fit_transform(X).toarray()
# print(one_hot_encoder.n_values_)
# print(one_hot_encoder.feature_indices_)
# print(X)
import seaborn as sns
#Importing train data
train = pd.read_csv("titanic_train.csv")
#Splitting the train dataset
train.drop('Cabin', axis=1, inplace=True)
x_train = train.drop('Survived', axis=1)
y_train = train['Survived']
#Visualising train data for null values
sns.heatmap(x_train.isnull())
#Filling the missing values of Age column in train dataset with its mean.
from sklearn.preprocessing import Imputer
imputer_train = Imputer(missing_values="NaN", strategy="mean", axis=0)
imputer_train = imputer_train.fit(x_train['Age'].values.reshape(-1, 1))
x_train['Age'] = imputer_train.transform(x_train['Age'].values.reshape(-1, 1))
#Visualising train data for null values
sns.heatmap(x_train.isnull())
#Importing test data
test = pd.read_csv('titanic_test.csv')
x_test = train.drop('Survived', axis=1)
y_test = train['Survived']
#Visualising test data for null values
sns.heatmap(x_test.isnull())
#Filling the missing values of Age column in test dataset with its mean.
import pandas as pd
import numpy as np
data = pd.read_excel("/home/karthik/Cauvery Study/dataset_rainfall_whole.xlsx")
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values=0, strategy='most_frequent', axis=0)
imputer = imputer.fit(X[:, [5, 6]])
X[:, [5, 6]] = imputer.transform(X[:, [5, 6]])
X1 = data.iloc[:, [0, 1, 2, 3, 4, 5, 6, 8]]
#imputer = imputer.fit(y[:,0:1])
#y[:,0:1] = imputer.transform(y[:,0:1])
#from sklearn.model_selection import train_test_split
#X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.1)
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators=10)
regressor.fit(X, y)
from tkinter import *
master = Tk()
y_pred = regressor.predict(X1).astype(int)
def imputer(X):
#fill in empty values
imp = Imputer(missing_values="NaN", strategy="most_frequent", axis=0)
imp = imp.fit(X[:, [1, 2, 3, -1]])
X[:, [1, 2, 3, -1]] = imp.transform(X[:, [1, 2, 3, -1]])
return X
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, Imputer, StandardScaler
train_data = pd.read_csv('train_data/data.csv', delimiter=',')
x = train_data[train_data.keys()[:-1]].values
y = train_data['result'].values
result_encoder = LabelEncoder()
result_encoder.fit(y)
y = result_encoder.transform(y)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
# Data normalization
imputer = Imputer(strategy='mean')
imputer.fit(x_train)
X_train = imputer.transform(x_train)
X_test = imputer.transform(x_test)
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
model = Sequential([
Dense(26, input_dim=x.shape[1]),
Activation('relu'),
Dense(1),
Activation('sigmoid')
])
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
np.set_printoptions(threshold=np.nan)
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 3].values
from sklearn.preprocessing import Imputer, LabelEncoder, OneHotEncoder
imputer = Imputer(missing_values='NaN', strategy="mean", axis=0)
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])
labelEncoder = LabelEncoder()
X[:, 0] = labelEncoder.fit_transform(X[:, 0])
oneHotEncoder_X = OneHotEncoder(categorical_features=[0])
X = oneHotEncoder_X.fit_transform(X).toarray()
labelEncoder_Y = LabelEncoder()
Y = labelEncoder_Y.fit_transform(Y)
dfg.sum()
df[df['R6'] > 1]
df[df.R6 > 1] = df.R6.mean()
df[df.R5 > 1] = df.R5.mean()
df[df.R19 > 1] = df.R19.mean()
df.max()
df.columns
X = df.drop(['object'], axis=1)
Y = df['object']
Y.iloc[12] = 'R'
Y.iloc[19] = 'R'
Y.iloc[200] = 'M'
#Removing NA Values
#Take care of Missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values="NaN", strategy='mean', axis=0)
imputer = imputer.fit(X)
X = imputer.transform(X)
#Converting Categorical data
#Encoding categorical data using dummy variables for X
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
onehotencoder = OneHotEncoder(categorical_features=[0])
Y = onehotencoder.fit_transform(Y).toarray()
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=0.3,
}
# Replacing Categorical Values with the Encoded Values
for col in col_names:
if df[col].dtype == 'O':
df[col] = df[col].replace(enc[col])
# Creating Separate Dataframes for Features and Class
X = df.iloc[:, :-1].values
y = df.iloc[:, 12].values
# Removing Loan_ID Column from the Dataset
X = np.delete(X, 0, 1)
# Creating Instances of Imputer Class for Missing Value Management
imputer_mode = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
imputer_mean = Imputer(missing_values='NaN', strategy='mean', axis=0)
# Replacing 'NaN' Values with Mode of the Values in the Respective Columns
X[:7] = imputer_mode.fit_transform(X[:7])
X[9:] = imputer_mode.fit_transform(X[9:])
# Replacing 'NaN' Values present in "LoanAmount" Column with the Mean of the Values of that Column
X_temp = X[:, 7].reshape(-1, 1)
X_temp = imputer_mean.fit_transform(X_temp)
X_temp = X_temp.reshape(1, -1)
X = np.delete(X, 7, 1)
X = np.insert(X, 7, X_temp, axis=1)
#----------------------------------------Data Preprocessing and Data Cleaning----------------------------------------
action='store_true',
help='Whether to use scikit data balancing by changing penalties '
'in learning algorithm formulation or manually balance by '
'undersampling majority class and oversampling minority class')
args = parser.parse_args()
if __name__ == "__main__":
# Let numpy know that NA corresponds to our missing value
data = numpy.genfromtxt(args.input_filename,
delimiter=",",
skip_header=1,
missing_values="NA",
filling_values="NaN")
# Impute the data and replace missing values
imputer = Imputer(missing_values="NaN",
strategy='mean',
axis=0,
copy=False)
imputer.fit(data)
data = imputer.transform(data)
features = data[:, 0:args.label_column]
labels = data[:, args.label_column].astype(int)
train_features, test_features, train_labels, test_labels = (
model_selection.train_test_split(features, labels, test_size=0.20))
# scale data only if model is linear (svm, logisitic regression) or scales of features
# are relevant (knn)
if args.algorithm in ['linear-svm', 'kernel-svm', 'logistic', 'knn']:
(train_features,
test_features) = utils.scale_data(train_features, test_features,
'minmax')
def __init__(self):
self.reg = Pipeline([
('imputer', Imputer(strategy='median')),
('regressor', RandomForestRegressor(n_estimators = 500, max_features=0.5, min_samples_leaf = 5))
])
from sklearn.ensemble import ExtraTreesClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import BernoulliNB
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import Imputer, Normalizer
from tpot.builtins import StackingEstimator
# NOTE: Make sure that the class is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1).values
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'].values, random_state=42)
imputer = Imputer(strategy="median")
imputer.fit(training_features)
training_features = imputer.transform(training_features)
testing_features = imputer.transform(testing_features)
# Score on the training set was:0.9348076424295388
exported_pipeline = make_pipeline(
StackingEstimator(
estimator=GradientBoostingClassifier(learning_rate=0.01,
max_depth=4,
max_features=0.05,
min_samples_leaf=10,
min_samples_split=19,
n_estimators=100,
subsample=0.9000000000000001)),
Normalizer(norm="max"),
@author: Ashlin
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
mydata = pd.read_csv('Data.csv')
print mydata.head()
print mydata.iloc[:, 0]
print mydata.head()
X = mydata.iloc[:, 0:3].values
y = mydata.iloc[:, 3].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
X[:, 1:3] = imputer.fit_transform(X[:, 1:3])
print X
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[:, 0] = labelencoder.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features=[0])
X = onehotencoder.fit_transform(X).toarray()
print X
y = labelencoder.fit_transform(y)
print y
print X[:, 2]
from sklearn.model_selection import train_test_split
def predict_catkit_demo(images):
"""Return a prediction of adsorption energies for structures generated with
CatKitDemo.
Parameters
----------
images : list
List of atoms objects representing adsorbate-surface structures.
model : str
Path and filename of Catlearn model pickle.
"""
model_ref = {'H': 'H2',
'O': 'H2O, H2',
'C': 'CH4, H2'}
# Make list of strings showing the references.
display_ref = []
for atoms in images:
try:
initial_state = [model_ref[s] for s in
ase.atoms.string2symbols(
atoms.info['key_value_pairs']['species'])]
except KeyError:
return {}
display_ref.append(
'*, ' + ', '.join(list(np.unique(initial_state))))
images = autogen_info(images)
gen = FeatureGenerator(nprocs=1)
train_fpv = default_fingerprinters(gen, 'adsorbates')
train_fpv = [gen.mean_chemisorbed_atoms,
gen.count_chemisorbed_fragment,
gen.count_ads_atoms,
gen.count_ads_bonds,
gen.ads_av,
gen.ads_sum,
gen.bulk,
gen.term,
gen.strain,
gen.mean_surf_ligands,
gen.mean_site,
gen.median_site,
gen.max_site,
gen.min_site,
gen.sum_site,
gen.generalized_cn,
gen.en_difference_ads,
gen.en_difference_chemi,
gen.en_difference_active,
gen.db_size,
gen.delta_energy]
matrix = gen.return_vec(images, train_fpv)
feature_index = np.load(clean_index_name)
clean_feature_mean = np.load(clean_mean)
impute = Imputer(missing_values="NaN", strategy='mean')
impute.statistics_ = clean_feature_mean
new_data = impute.transform(matrix[:, feature_index])
prediction = gp.predict(new_data,
get_validation_error=False,
get_training_error=False,
uncertainty=True)
output = {'mean': list(prediction['prediction']),
'uncertainty': list(prediction['uncertainty']),
'references': display_ref}
return output
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import Imputer
import xgboost as xgb
from ZK import ZKTools
# --------------------
print('Loading data...')
path = '/Users/Bing/Documents/DS/Zillow_Kaggle/'
df_train = pd.read_csv('train_features.csv')
df_target = pd.read_csv('train_target.csv').values.ravel()
imp = Imputer()
df_train_imp = pd.DataFrame(imp.fit_transform(df_train),
columns=df_train.columns)
n_estimators = 1
random_state = 0
# RF
rf_params = {
'n_jobs': -1,
'n_estimators': n_estimators,
'warm_start': True,
'max_depth': 6,
'min_samples_leaf': 2,
'max_features': 'sqrt',
'verbose': 0
}
rf = RandomForestRegressor(random_state=random_state, **rf_params)
rf_CV = ZKTools.CV(df_train=df_train_imp,
class NetworkClassifer():
def __init__(self, features, labels, validation_features,
validation_labels):
self.features = features
self.feature_labels = [
'min', 'max', 'mean', 'skew', 'std', 'kurtosis',
'sum of absolute difference', 'baseline_n', 'baseline_diff',
'baseline_diff_skew', 'n_pks', 'n_vals', 'av_pk', 'av_val',
'av pk val range', '1 hz', '5 hz', '10 hz', '15 hz', '20 hz',
'30 hz', '60 hz', '90 hz'
]
self.labels = np.ravel(labels)
self.validation_features = validation_features
self.validation_labels = np.ravel(validation_labels)
self.impute_and_scale()
def impute_and_scale(self):
print('Scaling and imputing training dataset...')
self.imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)
self.imputer.fit(self.features)
imputed_features = self.imputer.transform(self.features)
self.std_scaler = StandardScaler()
self.std_scaler.fit(imputed_features)
self.iss_features = self.std_scaler.transform(imputed_features)
print('Done')
print(
'Scaling and imputing validation features using training dataset...'
)
imputed_validation_features = self.imputer.transform(
self.validation_features)
self.iss_validation_features = self.std_scaler.transform(
imputed_validation_features)
print('Done')
def _cross_validation(self, clf, k_folds=5):
self.scores = cross_validation.cross_val_score(clf,
self.iss_features,
self.labels,
cv=k_folds,
n_jobs=5,
scoring='roc_auc')
def randomforest_info(self, max_trees=1000, step=40, k_folds=5):
print('Characterising R_forest. Looping through trees: ')
self.treedata = np.zeros((max_trees / step, 10))
for i, n_trees in enumerate(np.arange(0, max_trees, step)):
if n_trees == 0:
n_trees = 1
r_forest = RandomForestClassifier(
n_estimators=n_trees,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0,
)
scores = cross_validation.cross_val_score(r_forest,
self.iss_features,
self.labels,
cv=k_folds,
n_jobs=5)
r_forest_full = RandomForestClassifier(n_estimators=n_trees,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0)
r_forest_full.fit(self.iss_features, self.labels)
self.treedata[i, 0] = n_trees
self.treedata[i, 1] = scores.mean()
self.treedata[i, 2] = scores.std()
# now add the test dataset - score
self.treedata[i, 3] = r_forest_full.score(
self.iss_validation_features, self.validation_labels)
r_forest_lda = RandomForestClassifier(n_estimators=n_trees,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0)
r_forest_lda_full = RandomForestClassifier(n_estimators=n_trees,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0)
r_forest_lda_full.fit(self.lda_iss_features, self.labels)
lda_scores = cross_validation.cross_val_score(
r_forest_lda,
self.lda_iss_features,
self.labels,
cv=k_folds,
n_jobs=5)
self.treedata[i, 4] = lda_scores.mean()
self.treedata[i, 5] = lda_scores.std()
self.treedata[i, 6] = r_forest_lda_full.score(
self.lda_iss_validation_features, self.validation_labels)
r_forest_pca = RandomForestClassifier(n_estimators=n_trees,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0)
r_forest_pca_full = RandomForestClassifier(n_estimators=n_trees,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0)
r_forest_pca_full.fit(self.pca_iss_features, self.labels)
pca_scores = cross_validation.cross_val_score(
r_forest_pca,
self.pca_iss_features,
self.labels,
cv=k_folds,
n_jobs=5)
self.treedata[i, 7] = pca_scores.mean()
self.treedata[i, 8] = pca_scores.std()
self.treedata[i, 9] = r_forest_pca_full.score(
self.pca_iss_validation_features, self.validation_labels)
def pca(self, n_components=6):
self.pca = PCA(n_components)
self.pca_iss_features = self.pca.fit_transform(self.iss_features)
self.pca_iss_validation_features = self.pca.transform(
self.iss_validation_features)
def lda(self, n_components=2, pca_reg=True, reg_dimensions=10):
self.lda = LinearDiscriminantAnalysis(n_components=n_components,
solver='eigen',
shrinkage='auto')
#self.lda = LDA(n_components)
if pca_reg:
self.pca_reg = PCA(reg_dimensions)
pca_reg_features = self.pca_reg.fit_transform(self.iss_features)
self.lda_iss_features = self.lda.fit_transform(
pca_reg_features, self.labels)
pca_reg_validation_features = self.pca_reg.transform(
self.iss_validation_features)
self.lda_iss_validation_features = self.lda.transform(
pca_reg_validation_features)
else:
self.lda_iss_features = self.lda.fit_transform(
self.iss_features, self.labels)
self.lda_iss_validation_features = self.lda.transform(
self.iss_validation_features)
def lda_run(self, k_folds=5):
self.r_forest_lda = RandomForestClassifier(n_estimators=2000,
n_jobs=5,
max_depth=None,
min_samples_split=2,
random_state=7,
max_leaf_nodes=None,
min_samples_leaf=2,
criterion='gini',
max_features='sqrt',
class_weight='balanced')
self.lda_scores = cross_validation.cross_val_score(
self.r_forest_lda,
self.lda_iss_features,
self.labels,
cv=k_folds,
n_jobs=5)
print(
"Cross validation Random Forest performance LDA: Accuracy: %0.2f (std %0.2f)"
% (self.lda_scores.mean() * 100, self.lda_scores.std() * 100))
self.r_forest_lda.fit(self.lda_iss_features, self.labels)
print(
str(
self.r_forest_lda.score(self.lda_iss_validation_features,
self.validation_labels) * 100) +
'LDA test-set performance')
y_true = self.validation_labels
y_pred = self.r_forest_lda.predict(self.lda_iss_validation_features)
target_names = ['S1', 'S2', 'S3', 'S4']
report = classification_report(y_true,
y_pred,
target_names=target_names)
print('Random forest report lda')
print(report)
##### Hacky way to export features, so can optimise RF etc ######
train_X = pd.DataFrame(self.lda_iss_features)
train_y = pd.DataFrame(self.labels)
training = pd.concat([train_X, train_y], axis=1)
training.to_csv(
'/Users/Jonathan/Dropbox/Data_sharing_VMJC/training_lda.csv',
index=False)
test_X = pd.DataFrame(self.lda_iss_validation_features)
test_y = pd.DataFrame(self.validation_labels)
test = pd.concat([test_X, test_y], axis=1)
test.to_csv('/Users/Jonathan/Dropbox/Data_sharing_VMJC/test_lda.csv',
index=False)
train_X = pd.DataFrame(self.iss_features)
train_y = pd.DataFrame(self.labels)
training = pd.concat([train_X, train_y], axis=1)
training.to_csv(
'/Users/Jonathan/Dropbox/Data_sharing_VMJC/training.csv',
index=False)
test_X = pd.DataFrame(self.iss_validation_features)
test_y = pd.DataFrame(self.validation_labels)
test = pd.concat([test_X, test_y], axis=1)
test.to_csv('/Users/Jonathan/Dropbox/Data_sharing_VMJC/test.csv',
index=False)
def pca_run(self, k_folds=5):
self.r_forest_pca = RandomForestClassifier(n_estimators=2000,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0)
self.pca_scores = cross_validation.cross_val_score(
self.r_forest_pca,
self.pca_iss_features,
self.labels,
cv=k_folds,
n_jobs=5)
print(
"Cross validation RF performance PCA: Accuracy: %0.2f (std %0.2f)"
% (self.pca_scores.mean() * 100, self.pca_scores.std() * 100))
self.r_forest_pca.fit(self.pca_iss_features, self.labels)
print(
str(
self.r_forest_pca.score(self.pca_iss_validation_features,
self.validation_labels)) +
'PCA test-set performance ')
def run(self):
r_forest = RandomForestClassifier(n_estimators=2000,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0,
class_weight='balanced')
self._cross_validation(r_forest)
print("Cross validation RF performance: Accuracy: %0.2f (std %0.2f)" %
(self.scores.mean() * 100, self.scores.std() * 100))
self.r_forest = RandomForestClassifier(n_estimators=2000,
n_jobs=5,
max_depth=None,
min_samples_split=1,
random_state=0,
class_weight='balanced')
self.r_forest.fit(self.iss_features, self.labels)
print(
str(
self.r_forest.score(self.iss_validation_features,
self.validation_labels)) +
'randomforest test-set performance')
y_true = self.validation_labels
y_pred = self.r_forest.predict(self.iss_validation_features)
target_names = ['inter-ictal', 'ictal']
target_names = ['S1', 'S2', 'S3', 'S4']
t = classification_report(y_true, y_pred, target_names=target_names)
print('Random forest report:')
print(t)
return None
