可视化-python

<title>readme_energy_viz</title>

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模型构建（能耗可视化）¶

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

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In [2]:

from tqdm import tqdm_notebook as tqdm

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In [3]:

from pathlib import Path

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数据读入¶

In [4]:

data_dir = '/Users/douby/dataset/ytkc'

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In [5]:

data_files = list((Path(data_dir) / 'feature_agg_half').glob('./*.csv'))
data_files

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<div class="prompt output_prompt">Out[5]:</div>

[PosixPath('/Users/douby/dataset/ytkc/feature_agg_half/LZYTFTC25J1054033.csv'),
 PosixPath('/Users/douby/dataset/ytkc/feature_agg_half/LZYTATE64G1080906.csv'),
 PosixPath('/Users/douby/dataset/ytkc/feature_agg_half/LZYTFTC23J1054032.csv'),
 PosixPath('/Users/douby/dataset/ytkc/feature_agg_half/LZYTATE63H1023601.csv'),
 PosixPath('/Users/douby/dataset/ytkc/feature_agg_half/LZYTATE66G1080907.csv'),
 PosixPath('/Users/douby/dataset/ytkc/feature_agg_half/LZYTFTC21J1054031.csv')]

In [6]:

[print(pd.read_csv(df).shape) for df in data_files]

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<div class="prompt"></div>

(248, 13)
(10, 13)
(269, 13)
(375, 13)
(16, 13)
(90, 13)

<div class="prompt output_prompt">Out[6]:</div>

[None, None, None, None, None, None]

In [7]:

# data_dfs = [pd.read_csv(data_file) for data_file in [data_files[0], data_files[1], data_files[2], data_files[3], data_files[4], data_files[5]]]
data_dfs = [pd.read_csv(data_file) for data_file in [data_files[0], data_files[1], data_files[2], data_files[4], data_files[5]]]
# data_dfs = [pd.read_csv(data_file) for data_file in [data_files[3]]]
data_dfs[0].head()

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<div class="prompt output_prompt">Out[7]:</div>

	distance	avg_speed	acs	acs_idle	idle_speed	smooth_speed	without_gear	with_gear	economical_speed	sharp_turn	acceleration	deceleration
0	0.339039	-12.078710	0.259306	0.314533	0.165292	-0.239653	0.157602	-0.107825	0.000000	-0.000054	0.000166	-0.000122
1	0.614563	-2.027863	0.000000	0.000000	0.471228	-0.561469	0.026788	-0.104122	0.000000	0.001136	-0.000065	-0.000458
2	0.238495	-23.937845	0.000000	0.000000	-0.342079	-0.582975	0.000416	0.000339	0.127921	-0.000127	-0.000240	0.000206
3	-0.228120	2.818354	0.029315	0.071177	0.103766	-0.062362	-0.003305	0.000948	-0.003327	0.000078	0.000145	0.000126
4	-0.053401	32.830485	0.070601	0.048295	0.122727	-0.691631	0.114690	0.115410	-0.029081	-0.000064	0.000142	0.000004

In [8]:

# data_df = data_dfs[0].copy()
data_df = pd.concat(data_dfs).reset_index(drop=True)
data_df.head()

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<div class="prompt output_prompt">Out[8]:</div>

	distance	avg_speed	acs	acs_idle	idle_speed	smooth_speed	without_gear	with_gear	economical_speed	sharp_turn	acceleration	deceleration
0	0.339039	-12.078710	0.259306	0.314533	0.165292	-0.239653	0.157602	-0.107825	0.000000	-0.000054	0.000166	-0.000122
1	0.614563	-2.027863	0.000000	0.000000	0.471228	-0.561469	0.026788	-0.104122	0.000000	0.001136	-0.000065	-0.000458
2	0.238495	-23.937845	0.000000	0.000000	-0.342079	-0.582975	0.000416	0.000339	0.127921	-0.000127	-0.000240	0.000206
3	-0.228120	2.818354	0.029315	0.071177	0.103766	-0.062362	-0.003305	0.000948	-0.003327	0.000078	0.000145	0.000126
4	-0.053401	32.830485	0.070601	0.048295	0.122727	-0.691631	0.114690	0.115410	-0.029081	-0.000064	0.000142	0.000004

In [9]:

data_df.describe()

</div>

<div class="prompt output_prompt">Out[9]:</div>

	distance	avg_speed	acs	acs_idle	idle_speed	smooth_speed	without_gear	with_gear	over_speed	economical_speed	sharp_turn	acceleration	deceleration
count	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000
mean	0.229736	-6.440642	0.042807	-0.046857	-0.060265	-0.179363	-0.003372	0.006495	0.000290	0.012537	-0.000126	-0.000056	0.000020
std	0.489846	28.344863	0.196066	0.128160	0.158991	0.502956	0.063343	0.165652	0.003622	0.210383	0.000283	0.000377	0.000121
min	-0.426985	-53.604094	-0.994052	-0.660708	-0.529819	-3.088889	-0.176723	-2.742593	-0.015642	-0.789326	-0.004440	-0.001327	-0.001001
25%	-0.036451	-34.058430	-0.045746	-0.121145	-0.165821	-0.565963	-0.042504	-0.068378	0.000000	-0.123138	-0.000224	-0.000312	-0.000039
50%	0.148542	-3.626165	0.000000	-0.017720	-0.039279	-0.238453	0.000000	0.005794	0.000000	0.008953	-0.000072	-0.000044	0.000018
75%	0.396871	20.102755	0.105704	0.016907	0.042837	0.227981	0.034733	0.098355	0.000000	0.169764	0.000008	0.000192	0.000086
max	8.932278	44.573739	1.556257	0.387177	0.471228	0.834543	0.467464	0.334345	0.047686	0.621732	0.001136	0.001022	0.000394

特征预处理¶

In [10]:

from plotly.offline import init_notebook_mode, iplot
from plotly import graph_objs as go
import plotly.express as px
init_notebook_mode(connected=True)

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<div class="prompt"></div>

In [11]:

col_min_maxs = []

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y（每升油可行驶距离）的分布¶

In [12]:

iplot(px.histogram(data_df, x='distance', histnorm='probability'))

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<div class="prompt"></div>

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In [13]:

col_min_maxs.append([-0.4, 1.2])

</div>

平均时速与85km/h之差的分布¶

In [14]:

iplot(px.histogram(data_df, x='avg_speed', histnorm='probability'))

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                    {"responsive": true}
                ).then(function(){

// Listen for the removal of the full notebook cells
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}}

// Listen for the clearing of the current output cell
var outputEl = gd.closest('.output');
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x.observe(outputEl, {childList: true});
}}

                    })
            };
            });
        </script>
    </div>

In [15]:

col_min_maxs.append([-70, 60])

</div>

空调开启¶

In [16]:

iplot(px.histogram(data_df, x='acs', histnorm='probability'))

</div>

<div class="prompt"></div>

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    </div>

In [17]:

col_min_maxs.append([-0.2, 0.3])

</div>

怠速（开启空调）¶

In [18]:

iplot(px.histogram(data_df, x='acs_idle', histnorm='probability'))

</div>

<div class="prompt"></div>

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In [19]:

col_min_maxs.append([-0.3, 0.3])

</div>

怠速（未开启空调）¶

In [20]:

iplot(px.histogram(data_df, x='idle_speed', histnorm='probability'))

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    </div>

In [21]:

col_min_maxs.append([-0.7, 0.4])

</div>

平稳驾驶¶

In [22]:

iplot(px.histogram(data_df, x='smooth_speed', histnorm='probability'))

</div>

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In [23]:

col_min_maxs.append([-1, 1])

</div>

空档滑行¶

In [24]:

iplot(px.histogram(data_df, x='without_gear', histnorm='probability'))

</div>

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    </div>

In [25]:

col_min_maxs.append([-0.06, 0.06])

</div>

带档滑行¶

In [26]:

iplot(px.histogram(data_df, x='with_gear', histnorm='probability'))

</div>

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In [27]:

col_min_maxs.append([-0.02, 0.05])

</div>

In [28]:

data_df.describe()

</div>

<div class="prompt output_prompt">Out[28]:</div>

	distance	avg_speed	acs	acs_idle	idle_speed	smooth_speed	without_gear	with_gear	over_speed	economical_speed	sharp_turn	acceleration	deceleration
count	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000	633.000000
mean	0.229736	-6.440642	0.042807	-0.046857	-0.060265	-0.179363	-0.003372	0.006495	0.000290	0.012537	-0.000126	-0.000056	0.000020
std	0.489846	28.344863	0.196066	0.128160	0.158991	0.502956	0.063343	0.165652	0.003622	0.210383	0.000283	0.000377	0.000121
min	-0.426985	-53.604094	-0.994052	-0.660708	-0.529819	-3.088889	-0.176723	-2.742593	-0.015642	-0.789326	-0.004440	-0.001327	-0.001001
25%	-0.036451	-34.058430	-0.045746	-0.121145	-0.165821	-0.565963	-0.042504	-0.068378	0.000000	-0.123138	-0.000224	-0.000312	-0.000039
50%	0.148542	-3.626165	0.000000	-0.017720	-0.039279	-0.238453	0.000000	0.005794	0.000000	0.008953	-0.000072	-0.000044	0.000018
75%	0.396871	20.102755	0.105704	0.016907	0.042837	0.227981	0.034733	0.098355	0.000000	0.169764	0.000008	0.000192	0.000086
max	8.932278	44.573739	1.556257	0.387177	0.471228	0.834543	0.467464	0.334345	0.047686	0.621732	0.001136	0.001022	0.000394

超速¶

In [29]:

iplot(px.histogram(data_df[data_df['over_speed'].abs() < 0.0005], x='over_speed', histnorm='probability'))

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In [30]:

col_min_maxs.append(None)

</div>

经济速度¶

In [31]:

iplot(px.histogram(data_df, x='economical_speed', histnorm='probability'))

</div>

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In [32]:

col_min_maxs.append([-0.35,  0.4])

</div>

急转弯¶

In [33]:

iplot(px.histogram(data_df, x='sharp_turn', histnorm='probability'))

</div>

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In [34]:

col_min_maxs.append([-0.0006,  0.00025])

</div>

急加速¶

In [35]:

iplot(px.histogram(data_df, x='acceleration', histnorm='probability'))

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    </div>

急减速¶

In [36]:

iplot(px.histogram(data_df, x='deceleration', histnorm='probability'))

</div>

<div class="prompt"></div>

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                    {"responsive": true}
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x.observe(outputEl, {childList: true});
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    </div>

In [37]:

col_min_maxs.append(None)

</div>

In [38]:

print(col_min_maxs)

</div>

<div class="prompt"></div>

[[-0.4, 1.2], [-70, 60], [-0.2, 0.3], [-0.3, 0.3], [-0.7, 0.4], [-1, 1], [-0.06, 0.06], [-0.02, 0.05], None, [-0.35, 0.4], [-0.0006, 0.00025], None]

数据切割¶

In [50]:

data_x = data_x[data_x['distance'].abs() < 0.5].reset_index(drop=True)

</div>

In [51]:

data_x.shape

</div>

<div class="prompt output_prompt">Out[51]:</div>

(453, 11)

In [52]:

iplot(px.histogram(data_x, x='acs_idle', histnorm='probability'))

</div>

<div class="prompt"></div>

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In [54]:

min_maxs

</div>

<div class="prompt output_prompt">Out[54]:</div>

{'avg_speed': [-51.621932734195525, 41.412472523975026],
 'idle_speed': [-0.4410424367192252, 0.28424100553722353],
 'smooth_speed': [-1.4261626899586215, 0.6907720409622349],
 'without_gear': [-0.1444684040642318, 0.1389533952208047],
 'with_gear': [-0.20398162286404914, 0.23038489260487244]}

In [56]:

stds = [col for col in data_x.columns if col not in list(min_maxs) + ['distance']]
stds

</div>

<div class="prompt output_prompt">Out[56]:</div>

['acs', 'acs_idle', 'economical_speed', 'sharp_turn', 'acceleration']

In [57]:

data_x.describe()

</div>

<div class="prompt output_prompt">Out[57]:</div>

	distance	avg_speed	acs	acs_idle	idle_speed	smooth_speed	without_gear	with_gear	economical_speed	sharp_turn	acceleration
count	453.000000	453.000000	453.000000	453.000000	453.000000	453.000000	453.000000	453.000000	453.000000	453.000000	453.000000
mean	0.104706	-0.850899	0.039484	-0.030939	-0.046611	-0.255106	0.001487	0.035037	-0.020283	-0.000079	-0.000006
std	0.203774	26.753502	0.155878	0.105407	0.144603	0.404190	0.054409	0.094853	0.190077	0.000152	0.000334
min	-0.426985	-51.520323	-0.335255	-0.330399	-0.436964	-1.406471	-0.138302	-0.203982	-0.514268	-0.000695	-0.000940
25%	-0.060484	-26.207044	-0.044206	-0.095439	-0.145260	-0.574704	-0.033859	-0.036968	-0.152447	-0.000182	-0.000219
50%	0.096973	3.548778	0.001166	-0.005849	-0.019430	-0.350080	0.001730	0.042694	-0.033392	-0.000047	0.000000
75%	0.269420	22.896486	0.103145	0.025409	0.053965	0.048863	0.038242	0.115736	0.129223	0.000030	0.000216
max	0.498554	41.412473	0.676811	0.286205	0.284241	0.679493	0.138953	0.230385	0.428343	0.000245	0.000767

In [58]:

minmax_scaler = MinMaxScaler()
std_scaler = StandardScaler()

</div>

In [59]:

data_x['acs'].std() ** 2

</div>

<div class="prompt output_prompt">Out[59]:</div>

0.024297971088833834

In [60]:

x1 = data_x[min_maxs.keys()].values
x2 = data_x[stds].values

</div>

In [61]:

x1 = minmax_scaler.fit_transform(x1)
x2 = std_scaler.fit_transform(x2)

</div>

In [62]:

minmax_scaler.data_min_

</div>

<div class="prompt output_prompt">Out[62]:</div>

array([-51.52032262,  -0.43696419,  -1.4064708 ,  -0.13830228,
        -0.20398162])

In [63]:

minmax_scaler.data_max_

</div>

<div class="prompt output_prompt">Out[63]:</div>

array([41.41247252,  0.28424101,  0.67949319,  0.1389534 ,  0.23038489])

In [64]:

std_scaler.var_

</div>

<div class="prompt output_prompt">Out[64]:</div>

array([2.42443332e-02, 1.10860194e-02, 3.60496195e-02, 2.29959783e-08,
       1.11296355e-07])

In [65]:

std_scaler.mean_

</div>

<div class="prompt output_prompt">Out[65]:</div>

array([ 3.94835887e-02, -3.09393541e-02, -2.02834151e-02, -7.88421458e-05,
       -6.35169372e-06])

In [66]:

# std_scaler.var_

</div>

In [67]:

# print(x1.shape, x2.shape)

</div>

In [68]:

x = np.concatenate([x1, x2], axis=1)
# x = x2

</div>

In [69]:

# x = (data_x - data_x.mean()).iloc[:, 1:].values

</div>

In [70]:

after_df = pd.DataFrame(x, columns=list(min_maxs.keys()) + stds)
print(after_df.shape, data_x.shape)
after_df['distance'] = data_x['distance']
for col in stds:
    after_df = after_df[after_df[col].abs() <= 3].reset_index(drop=True)
after_df.describe()

</div>

<div class="prompt"></div>

(453, 10) (453, 11)

<div class="prompt output_prompt">Out[70]:</div>

	avg_speed	idle_speed	smooth_speed	without_gear	with_gear	acs	acs_idle	economical_speed	sharp_turn	acceleration	distance
count	440.000000	440.000000	440.000000	440.000000	440.000000	440.000000	440.000000	440.000000	440.000000	440.000000	440.000000
mean	0.544840	0.540288	0.551291	0.505293	0.550379	-0.072003	0.014984	-0.003437	0.016661	-0.002541	0.102004
std	0.287738	0.200288	0.193503	0.195712	0.219310	0.876400	0.972739	0.999704	0.961396	0.996219	0.202384
min	0.000000	0.000000	0.000000	0.000000	0.000000	-2.406705	-2.844141	-2.601735	-2.753512	-2.797615	-0.426985
25%	0.275532	0.402921	0.399683	0.378846	0.374730	-0.547633	-0.577304	-0.714329	-0.656314	-0.636992	-0.058288
50%	0.590539	0.574636	0.504421	0.506732	0.567631	-0.253578	0.243660	-0.069439	0.206128	0.019039	0.096232
75%	0.799941	0.680800	0.698807	0.636874	0.737920	0.345690	0.537584	0.782284	0.718773	0.656972	0.253238
max	1.000000	1.000000	1.000000	1.000000	1.000000	2.891058	2.827787	2.362839	2.138621	2.319161	0.498554

In [71]:

after_df.shape

</div>

<div class="prompt output_prompt">Out[71]:</div>

(440, 11)

In [72]:

for col in stds:
    after_df[col] = after_df[col] / 6 + 0.5

</div>

In [73]:

from sklearn.linear_model import LogisticRegression
from sklearn import linear_model

</div>

In [74]:

iplot(px.histogram(after_df, x='distance', histnorm='probability'))

</div>

<div class="prompt"></div>

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                    {"barmode": "relative", "height": 600, "legend": {"tracegroupgap": 0}, "margin": {"t": 60}, "template": {"data": {"bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "bar"}], "barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}}, "type": "barpolar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], 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[0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2}}}, "xaxis": {"anchor": "y", "domain": [0.0, 0.98], "title": {"text": "distance"}}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "title": {"text": "count"}}},
                    {"responsive": true}
                ).then(function(){

// Listen for the removal of the full notebook cells
var notebookContainer = gd.closest('#notebook-container');
if (notebookContainer) {{
x.observe(notebookContainer, {childList: true});
}}

// Listen for the clearing of the current output cell
var outputEl = gd.closest('.output');
if (outputEl) {{
x.observe(outputEl, {childList: true});
}}

                    })
            };
            });
        </script>
    </div>

In [75]:

X = after_df.iloc[:, :-1]
y = after_df.loc[:, 'distance'] - after_df.loc[:, 'distance'].mean()
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=53)

</div>

In [76]:

reg = linear_model.LinearRegression()

</div>

In [77]:

reg.fit(X_train, y_train)

</div>

<div class="prompt output_prompt">Out[77]:</div>

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

In [78]:

after_df.head()

</div>

<div class="prompt output_prompt">Out[78]:</div>

	avg_speed	idle_speed	smooth_speed	without_gear	with_gear	acs	acs_idle	economical_speed	sharp_turn	acceleration	distance
0	0.296800	0.131564	0.394779	0.500326	0.470388	0.457737	0.548975	0.630095	0.446732	0.383276	0.238495
1	0.584709	0.749760	0.644359	0.486905	0.471790	0.489116	0.661643	0.514885	0.672521	0.575834	-0.228120
2	0.907654	0.776050	0.342690	0.912486	0.735304	0.533308	0.625423	0.492278	0.516407	0.574239	-0.053401
3	0.239374	0.579867	0.844144	0.258733	0.232203	0.457737	0.548975	0.618365	0.550169	0.433052	0.138776
4	0.852846	0.527447	0.430869	0.671148	0.656214	0.457737	0.548975	0.203461	0.457666	0.681878	-0.068668

In [79]:

reg.coef_

</div>

<div class="prompt output_prompt">Out[79]:</div>

array([-0.28016056, -0.13911922, -0.1505462 , -0.02798608,  0.00107875,
        0.06146868, -0.3391931 , -0.21884858, -0.20782035, -0.32308545])

In [80]:

from scipy.stats import pearsonr
for idx, col in enumerate(data_df.columns[1:]):
    print('{:>20s}: {} {}'.format(col, idx + 1, pearsonr(data_df.loc[:, 'distance'], data_df.loc[:, col])))

</div>

<div class="prompt"></div>

           avg_speed: 1 (-0.4806056935158062, 6.771838194917686e-38)
                 acs: 2 (0.025909353896178537, 0.5152485744583909)
            acs_idle: 3 (-0.3700028295037353, 5.705255667186981e-22)
          idle_speed: 4 (-0.3228488553373969, 8.032180151372731e-17)
        smooth_speed: 5 (0.2785124836395245, 9.67637030815978e-13)
        without_gear: 6 (-0.2567446649900016, 5.489270993302441e-11)
           with_gear: 7 (-0.2168626382253659, 3.566268286835245e-08)
          over_speed: 8 (0.046565596512352606, 0.24204694520483655)
    economical_speed: 9 (0.3582354174754291, 1.32959542617395e-20)
          sharp_turn: 10 (-0.49814240667459775, 5.278312542744376e-41)
        acceleration: 11 (-0.49021488077200454, 1.4122058291001258e-39)
        deceleration: 12 (-0.07419675772517359, 0.06209243092334431)

In [81]:

from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error

</div>

In [82]:

mean_absolute_error(y_test, reg.predict(X_test))

</div>

<div class="prompt output_prompt">Out[82]:</div>

0.09102681542495218

In [83]:

r2_score(y_test, reg.predict(X_test))

</div>

<div class="prompt output_prompt">Out[83]:</div>

0.6527850673214266

In [84]:

after_df.head()

</div>

<div class="prompt output_prompt">Out[84]:</div>

	avg_speed	idle_speed	smooth_speed	without_gear	with_gear	acs	acs_idle	economical_speed	sharp_turn	acceleration	distance
0	0.296800	0.131564	0.394779	0.500326	0.470388	0.457737	0.548975	0.630095	0.446732	0.383276	0.238495
1	0.584709	0.749760	0.644359	0.486905	0.471790	0.489116	0.661643	0.514885	0.672521	0.575834	-0.228120
2	0.907654	0.776050	0.342690	0.912486	0.735304	0.533308	0.625423	0.492278	0.516407	0.574239	-0.053401
3	0.239374	0.579867	0.844144	0.258733	0.232203	0.457737	0.548975	0.618365	0.550169	0.433052	0.138776
4	0.852846	0.527447	0.430869	0.671148	0.656214	0.457737	0.548975	0.203461	0.457666	0.681878	-0.068668

In [85]:

X = after_df.iloc[:, [0, 1, 2, 3, 5, 6, 7, 8, 9]]
y = after_df.loc[:, 'distance']
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=53)

</div>

In [86]:

X.columns

</div>

<div class="prompt output_prompt">Out[86]:</div>

Index(['avg_speed', 'idle_speed', 'smooth_speed', 'without_gear', 'acs',
       'acs_idle', 'economical_speed', 'sharp_turn', 'acceleration'],
      dtype='object')

In [87]:

reg.fit(X_train, y_train)
reg.coef_

</div>

<div class="prompt output_prompt">Out[87]:</div>

array([-0.27961133, -0.13924411, -0.15054304, -0.02811706,  0.06150276,
       -0.33922744, -0.2191993 , -0.20771092, -0.32300866])

In [88]:

mean_absolute_error(y_test, reg.predict(X_test))

</div>

<div class="prompt output_prompt">Out[88]:</div>

0.09103503726488799

In [89]:

r2_score(y_test, reg.predict(X_test))

</div>

<div class="prompt output_prompt">Out[89]:</div>

0.6526890643994555

</div> </div> </div> </body>

posted @ 2020-02-21 15:58 亦神阅读(178) 评论(0) 收藏举报

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三旬老汉一枚

可视化-python

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特征预处理¶

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平均时速与85km/h之差的分布¶

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怠速（开启空调）¶

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