Python資料分析與機器學習-Python時間序列分析
阿新 • • 發佈：2019-01-19
原始碼下載地址：
http://download.csdn.net/download/adam_zs/10224873
from __future__ import absolute_import, division, print_function # http://www.lfd.uci.edu/~gohlke/pythonlibs/#xgboost import sys import os import pandas as pd import numpy as np import statsmodels.api as sm import statsmodels.formula.api as smf import statsmodels.tsa.api as smt import matplotlib.pylab as plt import seaborn as sns pd.set_option('display.height', 9999) pd.set_option('display.max_rows', 9999) pd.set_option('display.max_columns', 9999) pd.set_option('display.width', 9999) pd.set_option('display.float_format', lambda x: '%.5f' % x) # pandas np.set_printoptions(precision=5, suppress=True) # numpy pd.set_option('display.max_columns', 100) pd.set_option('display.max_rows', 100) # seaborn plotting style sns.set(style='ticks', context='poster') sentiment = pd.read_csv("data/sentiment.csv", index_col=0, parse_dates=[0]) # print(sentiment.head()) ''' DATE UMCSENT 2000-01-01 112.00000 2000-02-01 111.30000 2000-03-01 107.10000 2000-04-01 109.20000 2000-05-01 110.70000 ''' sentiment_short = sentiment['2005':'2016'] # sentiment_short.plot(figsize=(12, 8)) # plt.title("Consumer Sentiment") # plt.show() # sentiment_short = sentiment['2005':'2016'] # sentiment_short['diff_1'] = sentiment_short['UMCSENT'].diff(1) # 1階差分 # sentiment_short['diff_2'] = sentiment_short['diff_1'].diff(1) # 2階差分 # sentiment_short.plot(figsize=(18, 12)) # plt.title('diff_1 and diff_2') # plt.show() # acf結果,pacf結果 # fig = plt.figure(figsize=(12, 8)) # # ax1 = fig.add_subplot(211) # fig = sm.graphics.tsa.plot_acf(sentiment_short, lags=20, ax=ax1) # ax1.xaxis.set_ticks_position('bottom') # fig.tight_layout() # # ax2 = fig.add_subplot(212) # fig = sm.graphics.tsa.plot_pacf(sentiment_short, lags=20, ax=ax2) # ax2.xaxis.set_ticks_position('bottom') # fig.tight_layout() # plt.show() # 散點圖也可以表示 # lags = 9 # ncols = 3 # nrows = int(np.ceil(lags / ncols)) # # fig, axes = plt.subplots(ncols=ncols, nrows=nrows, figsize=(4 * ncols, 4 * nrows)) # # for ax, lag in zip(axes.flat, np.arange(1, lags + 1, 1)): # lag_str = 't-{}'.format(lag) # X = (pd.concat([sentiment_short, sentiment_short.shift(-lag)], axis=1, # keys=['y'] + [lag_str]).dropna()) # # X.plot(ax=ax, kind='scatter', y='y', x=lag_str) # corr = X.corr().as_matrix()[0][1] # ax.set_ylabel('Original') # ax.set_title('Lag: {} (corr={:.2f})'.format(lag_str, corr)) # ax.set_aspect('equal') # sns.despine() # # fig.tight_layout() # plt.show() # 更直觀一些 def tsplot(y, lags=None, title='', figsize=(14, 8)): fig = plt.figure(figsize=figsize) layout = (2, 2) ts_ax = plt.subplot2grid(layout, (0, 0)) hist_ax = plt.subplot2grid(layout, (0, 1)) acf_ax = plt.subplot2grid(layout, (1, 0)) pacf_ax = plt.subplot2grid(layout, (1, 1)) y.plot(ax=ts_ax) ts_ax.set_title(title) y.plot(ax=hist_ax, kind='hist', bins=25) hist_ax.set_title('Histogram') smt.graphics.plot_acf(y, lags=lags, ax=acf_ax) smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax) [ax.set_xlim(0) for ax in [acf_ax, pacf_ax]] sns.despine() plt.tight_layout() return ts_ax, acf_ax, pacf_ax tsplot(sentiment_short, title='Consumer Sentiment', lags=36); plt.show()
from __future__ import absolute_import, division, print_function import sys import os import pandas as pd import numpy as np # TSA from Statsmodels import statsmodels.api as sm import statsmodels.formula.api as smf import statsmodels.tsa.api as smt # Display and Plotting import matplotlib.pylab as plt import seaborn as sns pd.set_option('display.height', 9999) pd.set_option('display.max_rows', 9999) pd.set_option('display.max_columns', 9999) pd.set_option('display.width', 9999) pd.set_option('display.float_format', lambda x: '%.5f' % x) # pandas np.set_printoptions(precision=5, suppress=True) # numpy pd.set_option('display.max_columns', 100) pd.set_option('display.max_rows', 100) # seaborn plotting style sns.set(style='ticks', context='poster') ts_df = pd.read_csv('data/series1.csv', index_col=0, parse_dates=[0]) # print(ts_df.head()) # print(ts_df.shape) (120, 1) ''' value 2006-06-01 0.21507 2006-07-01 1.14225 2006-08-01 0.08077 2006-09-01 -0.73952 2006-10-01 0.53552 ''' train_count = int(ts_df.shape[0] * 0.95) + 1 X_train = ts_df[:train_count]['value'] y_test = ts_df[train_count:]['value'] # print(X_train.shape) # print(X_train.tail()) # print(y_test.shape) # print(y_test.head()) def tsplot(y, lags=None, title='', figsize=(14, 8)): fig = plt.figure(figsize=figsize) layout = (2, 2) ts_ax = plt.subplot2grid(layout, (0, 0)) hist_ax = plt.subplot2grid(layout, (0, 1)) acf_ax = plt.subplot2grid(layout, (1, 0)) pacf_ax = plt.subplot2grid(layout, (1, 1)) y.plot(ax=ts_ax) ts_ax.set_title(title) y.plot(ax=hist_ax, kind='hist', bins=25) hist_ax.set_title('Histogram') smt.graphics.plot_acf(y, lags=lags, ax=acf_ax) smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax) [ax.set_xlim(0) for ax in [acf_ax, pacf_ax]] sns.despine() fig.tight_layout() return ts_ax, acf_ax, pacf_ax # tsplot(X_train, title='A Given Training Series', lags=20) # plt.show() arima200 = sm.tsa.SARIMAX(X_train, order=(2, 0, 0)) model_results = arima200.fit() import itertools p_min = 0 d_min = 0 q_min = 0 p_max = 4 d_max = 0 q_max = 4 # Initialize a DataFrame to store the results results_bic = pd.DataFrame(index=['AR{}'.format(i) for i in range(p_min, p_max + 1)], columns=['MA{}'.format(i) for i in range(q_min, q_max + 1)]) for p, d, q in itertools.product(range(p_min, p_max + 1), range(d_min, d_max + 1), range(q_min, q_max + 1)): if p == 0 and d == 0 and q == 0: results_bic.loc['AR{}'.format(p), 'MA{}'.format(q)] = np.nan continue try: model = sm.tsa.SARIMAX(X_train, order=(p, d, q), # enforce_stationarity=False, # enforce_invertibility=False, ) results = model.fit() results_bic.loc['AR{}'.format(p), 'MA{}'.format(q)] = results.bic except: continue results_bic = results_bic[results_bic.columns].astype(float) fig, ax = plt.subplots(figsize=(10, 8)) ax = sns.heatmap(results_bic, mask=results_bic.isnull(), ax=ax, annot=True, fmt='.2f', ) ax.set_title('BIC') plt.show()
import matplotlib.pylab import numpy as np import pandas as pd '''滑動視窗''' df = pd.Series(np.random.randint(low=1, high=100, size=600), index=pd.date_range(start='2016-01-07', periods=600, freq='D')) # print(df.head()) print(df.rolling(window=10)) # 滑動視窗 import matplotlib.pyplot as plt plt.figure(figsize=(15, 5)) df.plot(style='r--') df.rolling(window=10).mean().plot(style='b') plt.show()
import pandas as pd
import numpy as np
rng = pd.date_range('2017-01-05', periods=90, freq='D')
ts = pd.Series(np.random.randint(low=1, high=20, size=90), index=rng)
# print(ts)
# print(ts.resample(rule="M").sum())
day3Ts = ts.resample(rule='3D').sum()
# print(day3Ts.resample(rule='D').asfreq())
'''
ffill 空值取前面的值
bfill 空值取後面的值
interpolate 線性取值
'''
print(day3Ts.resample(rule='D').ffill(1)) # 1 對1個缺失值進行填充
import matplotlib.pylab as plt
import seaborn as sns
from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures
from tsfresh import extract_features, extract_relevant_features, select_features
from tsfresh.utilities.dataframe_functions import impute
from tsfresh.feature_extraction import ComprehensiveFCParameters
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
download_robot_execution_failures()
df, y = load_robot_execution_failures()
# df[df.id == 3][['time', 'F_x', 'F_y', 'F_z', 'T_x', 'T_y', 'T_z']].plot(x='time', title='Success example (id 3)',
# figsize=(12, 6))
# df[df.id == 20][['time', 'F_x', 'F_y', 'F_z', 'T_x', 'T_y', 'T_z']].plot(x='time', title='Failure example (id 20)',
# figsize=(12, 6))
# plt.show()
extraction_settings = ComprehensiveFCParameters() # 特徵提取
X = extract_features(df,
column_id='id', column_sort='time',
default_fc_parameters=extraction_settings,
impute_function=impute)
# 特徵過濾
X_filtered = extract_relevant_features(df, y,
column_id='id', column_sort='time',
default_fc_parameters=extraction_settings)
X_train, X_test, X_filtered_train, X_filtered_test, y_train, y_test = train_test_split(X, X_filtered, y, test_size=.4)
cl = DecisionTreeClassifier()
cl.fit(X_train, y_train)
cl2 = DecisionTreeClassifier()
cl2.fit(X_filtered_train, y_train)
print(classification_report(y_test, cl2.predict(X_filtered_test)))
'''維基百科點選量資料'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import re
pd.set_option('display.height', 9999)
pd.set_option('display.max_rows', 9999)
pd.set_option('display.max_columns', 9999)
pd.set_option('display.width', 9999)
train = pd.read_csv('train_1.csv').fillna(0) # fillna空缺資料填充為0
# print(train.info())
for col in train.columns[1:]:
train[col] = pd.to_numeric(train[col], downcast='integer')
# print(train.head())
# print(train.info())
'''
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'''
def get_language(page):
res = re.search('[a-z][a-z].wikipedia.org', page)
if res:
return res.group()[0:2]
else:
return 'na'
train['lang'] = train['Page'].map(get_language)
# print(train.head())
from collections import Counter
# print(Counter(train['lang']))
# Counter({'zh': 19, 'en': 13, 'ja': 11, 'ru': 10, 'de': 8, 'fr': 7, 'es': 6})
lang_sets = {}
lang_sets['en'] = train[train.lang == 'en'].iloc[:, :-1]
lang_sets['ja'] = train[train.lang == 'ja'].iloc[:, :-1]
lang_sets['de'] = train[train.lang == 'de'].iloc[:, :-1]
lang_sets['fr'] = train[train.lang == 'fr'].iloc[:, :-1]
lang_sets['zh'] = train[train.lang == 'zh'].iloc[:, :-1]
lang_sets['ru'] = train[train.lang == 'ru'].iloc[:, :-1]
lang_sets['es'] = train[train.lang == 'es'].iloc[:, :-1]
sums = {}
for key in lang_sets:
sums[key] = lang_sets[key].iloc[:, 1:].sum(axis=0) / lang_sets[key].shape[0]
# print(sums)
days = [r for r in range(sums['en'].shape[0])]
# 不同國家詞頻的點選情況
# fig = plt.figure(1, figsize=[10, 10])
# plt.ylabel('Views per Page')
# plt.xlabel('Day')
# plt.title('Pages in Different Languages')
# labels = {'en': 'English', 'ja': 'Japanese', 'de': 'German', 'fr': 'French',
# 'zh': 'Chinese', 'ru': 'Russian', 'es': 'Spanish'
# }
# for key in sums:
# plt.plot(days, sums[key], label=labels[key])
# plt.legend()
# plt.show()
# 不同詞條的點選量
# def plot_entry(key, idx):
# data = lang_sets[key].iloc[idx, 1:]
# fig = plt.figure(1, figsize=(10, 5))
# plt.plot(days, data)
# plt.xlabel('day')
# plt.ylabel('views')
# plt.title(train.iloc[lang_sets[key].index[idx], 0])
# plt.show()
#
#
# for idx in range(5, 10):
# plot_entry('en', idx)
# 檢視每個國家關注的熱點
top_pages = {} # 每個國家top1熱點
def national_hot(key):
sum_set = pd.DataFrame(lang_sets[key][['Page']])
sum_set['total'] = lang_sets[key].sum(axis=1)
sum_set = sum_set.sort_values(by='total', ascending=False)
top_pages[key] = sum_set.index[0]
print('-----', key, '-----')
print(sum_set.head(5))
for key in lang_sets:
national_hot(key)
# 每個國家最熱的話題
for key in top_pages:
fig = plt.figure(1, figsize=(10, 5))
cols = train.columns
cols = cols[1:-1]
data = train.loc[top_pages[key], cols]
plt.plot(days, data)
plt.xlabel('Days')
plt.ylabel('Views')
plt.title(train.loc[top_pages[key], 'Page'])
plt.show()
import numpy as np
import pandas as pd
# rng = pd.date_range(start='2017-01-01', periods=10, freq='3D')
# print(rng)
# print(np.random.randint(low=1, high=20, size=10))
time = pd.Series(np.random.randint(low=1, high=20, size=10),
index=pd.date_range(start='2017-01-02', periods=10, freq='3D'))
time = time.truncate(before='2017-01-05')
# print(time)
# print(time['2017-01-05':'2017-01-20'])
p1 = pd.period_range('2016-01-01 10:10', freq='10H', periods=10)
# print(p1)
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