def standrand_data():
lrmfc_data_path = get_path('section1', 'LRFMC_data_path')
data = pd.read_excel(lrmfc_data_path)
data = (data - data.mean(axis=0)) / (data.std(axis=0))
data.columns = ['Z' + i for i in data.columns]
zscored_data_path = get_path('section1', 'zscored_data_path')
data.to_excel(zscored_data_path, index=False)
def clean_data():
source_data_path = get_path('section1', 'source_data_path')
clean_data_path = get_path('section1', 'clean_data_path')
data = pd.read_csv(source_data_path, encoding='utf-8')
data = data[data['SUM_YR_1'].notnull() * data['SUM_YR_2'].notnull()]
index1 = data['SUM_YR_1'] != 0
index2 = data['SUM_YR_2'] != 0
index3 = (data['SEG_KM_SUM'] == 0) & (data['avg_discount'] == 0)
data = data[index1 | index2 | index3]
data.to_excel(clean_data_path)
def largrange_interpolation():
inputfile = get_path('section1', 'inputfile')
outputfile = get_path('section2', 'outputfile')
data = pd.read_excel(inputfile, header=None)
# judge data if interpolation
for i in data.columns:
for j in range(len(data)):
if (data[i].isnull())[j]:
data[i][j] = ployinterp_column(data[i], j)
data.to_excel(outputfile, header=None, index=False)
def explore_data():
source_data_path = get_path('section1', 'source_data_path')
result_data_path = get_path('section1', 'result_data_path')
data = pd.read_csv(source_data_path, encoding='utf-8')
explore = data.describe(percentiles=[],
include='all').T # T is transposition
explore['null'] = len(data) - explore['count']
explore = explore[['null', 'max', 'min']]
explore.columns = [u'空值数', u'最大值', u'最小值']
explore.describe()
explore.to_excel(result_data_path)
def train_kmeans():
zscored_data_path = get_path('section1', 'zscored_data_path')
kmeans_file = get_path('section2', 'kmeans_file')
data = pd.read_excel(zscored_data_path)
kmodel = KMeans(n_clusters=5, n_jobs=4)
kmodel.fit(data)
# print(kmodel.cluster_centers_) ### five centres
# print(kmodel.inertia_)
kmodel.predict(data)
# print (kmodel.labels_)
joblib.dump(kmodel, kmeans_file)
def lm_model():
net_file = get_path('section2', 'net_file')
train = get_data()[0]
test = get_data()[1]
net = load_model(net_file)
predict_result = net.predict_classes(test[:, :3]).reshape(
len(test)) ## transform result
cm_plot(test[:, 3], predict_result).show()
def get_data():
model_data_path = get_path('section1', 'model_data_path')
model_data = pd.read_excel(model_data_path)
model_matrix = model_data.as_matrix()
shuffle(model_matrix)
p = 0.8 # ratio of training
train_data = model_matrix[:int(len(model_matrix) * p), :]
test_data = model_matrix[int(len(model_matrix) * p):, :]
print(train_data, test_data)
return train_data, test_data
def train_lm_classification():
# init net
net = Sequential()
netfile = get_path('section1', 'lm_net_file')
net.add(Dense(input_dim=3, output_dim=10)) # input to hide
net.add(Activation('relu')) # relu function between their
net.add(Dense(input_dim=10, output_dim=1)) # hide to output
net.add(Activation('sigmoid')) # sigmoid's function between their
net.compile(loss='binary_crossentropy', optimizer='adam') ## use adam
train = get_data()[0]
net.fit(train[:, :3], train[:, 3], nb_epoch=1000,
batch_size=1) # train model ,1000's loop
net.save(netfile)
def train_cart_classification():
treefile = get_path('section1', 'tree_file')
tree = DecisionTreeClassifier()
train = get_data()[0]
tree.fit(train[:, :3], train[:, 3])
joblib.dump(tree, treefile) # save training model by joblib
def cart_model():
tree_file = get_path('section2', 'tree_file')
train = get_data()[0]
test = get_data()[1]
cart = joblib.load(tree_file)
cm_plot(train[:, 3], cart.predict(train[:, :3])).show()
