def pcaPlot(filename):
subdir = filename.split('/')[-2]
columns, relevantColumns, labelNames, columnUnits, timestamps = getConfig(
subdir)
df = utilities.readDataFile(filename)
df = utilities.getDataWithTimeIndex(df)
df = df.dropna()
traintime, testtime, validtime = timestamps
if relevantColumns is not None:
df = utilities.dropIrrelevantColumns(df, [relevantColumns, labelNames])
start_train, end_train = traintime
start_test, end_test = testtime
start_valid, end_valid = validtime
df_train = utilities.getDataByTimeframe(df, start_train, end_train)
train_vals = df_train.values
#train_vals = df.values
sc = StandardScaler()
train_vals = sc.fit_transform(train_vals)
numberOfComponents = 2
pca = decomposition.PCA(n_components=numberOfComponents)
pca.fit(train_vals)
x = df.values
x = sc.transform(x)
x = pca.transform(x)
df_pca = pd.DataFrame(data=x, index=df.index, columns=['pca1', 'pca2'])
df_pca_train = utilities.getDataByTimeframe(df_pca, start_train, end_train)
df_pca_test = utilities.getDataByTimeframe(df_pca, end_train, end_test)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
ax.set_xlabel('PCA 1', fontsize=10)
ax.set_ylabel('PCA 2', fontsize=10)
ax.set_title('PCA plot', fontsize=12)
cmap = sns.cubehelix_palette(as_cmap=True)
indexx = list(range(df_pca_test.shape[0]))
ax.scatter(df_pca_train['pca1'], df_pca_train['pca2'], c='lightblue')
points = ax.scatter(df_pca_test['pca1'],
df_pca_test['pca2'],
c=indexx,
cmap=cmap,
alpha=0.4)
fig.colorbar(points)
plt.show()
return pca
def main(filename):
df = utilities.readDataFile(filename)
df = utilities.getDataWithTimeIndex(df)
df = df.dropna()
subdir = filename.split('/')[-2]
columns, relevantColumns, labelNames, columnUnits, timestamps = getConfig(subdir)
if relevantColumns is not None:
df = utilities.dropIrrelevantColumns(df, [relevantColumns, labelNames])
analysis.pairplot(df)
def main(filename, start, end):
df = utilities.readDataFile(filename)
df = utilities.getDataWithTimeIndex(df)
df = df.dropna()
df = utilities.getDataByTimeframe(df, start, end)
subdir = filename.split('/')[-2]
columns, relevantColumns, labelNames, columnUnits, timestamps = getConfig(
subdir)
if relevantColumns is not None:
df = utilities.dropIrrelevantColumns(df, [relevantColumns, labelNames])
prints.printDataframe(df)
def main(filename, numberOfComponents):
df = utilities.readDataFile(filename)
df = utilities.getDataWithTimeIndex(df)
df = df.dropna()
subdir = filename.split('/')[-2]
columns, relevantColumns, labelNames, columnUnits, timestamps = getConfig(
subdir)
if relevantColumns is not None:
df = utilities.dropIrrelevantColumns(df, [relevantColumns, labelNames])
prints.printEmptyLine()
pca = analysis.pca(df, numberOfComponents, relevantColumns, labelNames)
prints.printExplainedVarianceRatio(pca)
def main(filename):
df = utilities.readDataFile(filename)
df = utilities.getDataWithTimeIndex(df)
df = df.dropna()
subdir = filename.split('/')[-2]
columns, relevantColumns, labelNames, columnUnits, timestamps = getConfig(
subdir)
if relevantColumns is not None:
df = utilities.dropIrrelevantColumns(df, [relevantColumns, labelNames])
prints.printEmptyLine()
covMat = analysis.correlationMatrix(df)
prints.printCorrelationMatrix(covMat, df, labelNames)
def main(filename):
df = utilities.readDataFile(filename)
df = utilities.getDataWithTimeIndex(df)
df = df.dropna()
subdir = filename.split('/')[-2]
columns, relevantColumns, labelNames, columnUnits, timestamps = getConfig(
subdir)
traintime, testtime, validtime = timestamps
if relevantColumns is not None:
df = utilities.dropIrrelevantColumns(df, [relevantColumns, labelNames])
df_train, df_test = utilities.getTestTrainSplit(df, traintime, testtime)
analysis.valueDistribution(df_train, df_test)