def df_pca(df, n_comp=2):
rd_df = normalize_dataframe(df)
rd = PCA(n_components=n_comp)
rd.fit(caracteristicas_df)
caracteristicas_rd = rd.transform(rd_df)
caracteristicas_rd_df = pd.DataFrame(caracteristicas_rd)
return caracteristicas_rd_df
def df_mds(df, n_comp = 2, n_jobs = 1, max_iter = 1000):
rd_df = normalize_dataframe(df)
rd = MDS(n_components=n_comp, max_iter=max_iter, metric=True, n_jobs=n_jobs, random_state=2019)
# rd.fit(caracteristicas_df[:1000])
caracteristicas_rd = rd.fit_transform(caracteristicas_df)
caracteristicas_rd_df = pd.DataFrame(caracteristicas_rd)
return caracteristicas_rd_df
def df_isomap(df, n_comp = 2, n_jobs = 1, n_neighbors = 5, max_iter = 1000):
rd_df = normalize_dataframe(df)
rd = Isomap(n_components=n_comp, n_neighbors = n_neighbors, max_iter = max_iter )
rd.fit(caracteristicas_df)
caracteristicas_rd = rd.transform(rd_df)
caracteristicas_rd_df = pd.DataFrame(caracteristicas_rd)
return caracteristicas_rd_df
def df_kernel_pca(df, n_comp = 2, n_jobs = 1):
rd_df = normalize_dataframe(df)
rd = KernelPCA(kernel="rbf",n_components=n_comp, gamma=None, fit_inverse_transform=False, random_state = 2019, n_jobs=n_jobs)
rd.fit(caracteristicas_df)
caracteristicas_rd = rd.transform(rd_df)
caracteristicas_rd_df = pd.DataFrame(caracteristicas_rd)
return caracteristicas_rd_df
def df_svd(df, n_comp = 2, max_iter = 10):
rd_df = normalize_dataframe(df)
rd = TruncatedSVD(n_components=n_comp,algorithm='randomized', random_state=2019, n_iter=max_iter)
rd.fit(caracteristicas_df)
caracteristicas_rd = rd.transform(rd_df)
caracteristicas_rd_df = pd.DataFrame(caracteristicas_rd)
return caracteristicas_rd_df
