机器学习实验（一）：运用机器学习(Kmeans算法)判定家庭用电主因
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In [1]:
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltIn [2]:
sensor_data = pd.read_csv('merged-sensor-files.csv', names=["MTU", "Time", "Power", "Cost", "Voltage"], header = 0)weather_data = pd.read_json('weather.json', typ ='series')In [3]:
import jsonf=open('weather.json')json_data = json.load(f) Time = []Temperature = []for time, temperature in json_data.items(): Time.append(int(time)) Temperature.append(float(temperature)) temperature = pd.DataFrame({'Time':Time, 'Temperature': Temperature})temperatureOut[3]:
In [4]:
import jsonf=open('weather.json').read()Time = []Temperature = []for line in f.split(','): time, temperature = line.split(':') time = time.replace('"','') time = time.replace('{','') temperature = temperature.replace('"','') temperature = temperature.replace('}','') #print time, temperature Time.append(int(time)) Temperature.append(float(temperature))In [5]:
# A quick look at the datasetssensor_data.head(5)Out[5]:
In [6]:
sensor_data.describe()Out[6]:
In [7]:
sensor_data.info()In [8]:
sensor_data.dtypesOut[8]:
In [9]:
# Get the inconsistent rows indexesfaulty_row_idx = sensor_data[sensor_data["Power"] == " Power"].index.tolist()faulty_row_idxOut[9]:
In [10]:
sensor_data.drop(faulty_row_idx, inplace=True)sensor_data[sensor_data["Power"] == " Power"].index.tolist()Out[10]:
In [11]:
sensor_data[["Power", "Cost", "Voltage"]] = sensor_data[["Power", "Cost", "Voltage"]].astype(float)sensor_data[["Time"]] = pd.to_datetime(sensor_data["Time"])sensor_data['Hour'] = pd.DatetimeIndex(sensor_data["Time"]).hoursensor_data.dtypesOut[11]:
In [12]:
temperature_data = weather_data.to_frame()temperature_data.reset_index(level=0, inplace=True)temperature_data.columns = ["Time", "Temperature"]temperature_data.dtypestemperature_data['Temperature'] = Temperaturetemperature_data["Hour"] = pd.DatetimeIndex(temperature_data["Time"]).hourtemperature_data[["Temperature"]] = temperature_data[["Temperature"]].astype(float)temperature_dataOut[12]:
In [14]:
sensor_data.describe()Out[14]:
In [15]:
temperature_data.describe()Out[15]:
In [16]:
grouped_sensor_data = sensor_data.groupby(["Hour"], as_index = False).mean()grouped_sensor_dataOut[16]:
In [17]:
grouped_temperature_data = temperature_data.groupby(["Hour"], as_index = False).mean()grouped_temperature_dataOut[17]:
In [18]:
%pylab inlineplt.style.use('ggplot')In [19]:
fig = plt.figure(figsize=(13,7))plt.hist(sensor_data.Power, bins=50)fig.suptitle('Power Histogram', fontsize = 20)plt.xlabel('Power', fontsize = 16)plt.ylabel('Count', fontsize = 16)Out[19]:
In [20]:
fig = plt.figure(figsize=(13,7))plt.bar(grouped_sensor_data.Hour, grouped_sensor_data.Power)fig.suptitle('Power Distribution with Hours', fontsize = 20)plt.xlabel('Hour', fontsize = 16)plt.ylabel('Power', fontsize = 16)plt.xticks(range(0, 24))plt.show()
In [21]:
fig = plt.figure(figsize=(13,7))plt.bar(grouped_temperature_data.Temperature, grouped_sensor_data.Power)fig.suptitle('Power Distribution with Temperature', fontsize = 20)plt.xlabel('Temperature in Fahrenheit', fontsize = 16)plt.ylabel('Power', fontsize = 16)plt.show()
In [22]:
merged_data = grouped_sensor_data.merge(grouped_temperature_data)merged_dataOut[22]:
In [23]:
data =sensor_data.merge(grouped_temperature_data)data.drop(["Time", "MTU", "Cost", "Voltage"], axis = 1, inplace = True)data.head()Out[23]:
In [24]:
from sklearn.cluster import KMeansfrom sklearn.cross_validation import train_test_splitIn [25]:
np.random.seed(1234)train_data, test_data = train_test_split(data, test_size = 0.25, random_state = 42)In [26]:
train_data.shapeOut[26]:
In [27]:
test_data.shapeOut[27]:
In [28]:
kmeans = KMeans(n_clusters = 4, n_jobs = 4)kmeans_fit = kmeans.fit(train_data)In [29]:
predict = kmeans_fit.predict(test_data)In [30]:
test_data["Cluster"] = predicttest_data.head(20)Out[30]:
In [31]:
label_df = pd.DataFrame({"Cluster": [0, 1, 2, 3], "Appliances": ["Cooling System","Oven, Geyser", "Night Lights", "Home Security Systems"]})label_dfOut[31]:
In [32]:
result = test_data.merge(label_df)result.head()Out[32]:
In [33]:
result.tail()Out[33]:
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