From aa78bc92c9c75c60e71804d313666d2bf78f5b03 Mon Sep 17 00:00:00 2001 From: timonmerk Date: Tue, 15 Oct 2024 20:04:06 +0200 Subject: [PATCH] add new scripts --- combine_data_Catboost_multiple_timepoints.py | 74 + connectome_lead_mapper_compute.py | 105 + connectome_lead_mapper_write_data.py | 122 + figure_27_get_ch_ind_per_all_ch.py | 15 +- figure_29_LOSO_LOHO_ind.py | 77 + figure_30_RMAP_vs_no_RMAP.py | 85 + figure_31_how_much_data_is_needed.py | 75 + how_much_data_is_best_for_CatBoost.py | 159 + how_much_data_is_needed_bk.py | 140 + how_much_data_is_needed_tremor.py | 138 + ..._subject_out_not_hermisphere_validation.py | 49 +- merge_ucsf_features.py | 10 +- post_hoc_normalize_polars.py | 69 +- pyproject.toml | 1 - rmap_corr.csv | 55 - run_decoding_ucsf_across_patients.py | 60 +- uv.lock | 4368 +++++++++++++++++ write_mask_connectivity.m | 14 + 18 files changed, 5484 insertions(+), 132 deletions(-) create mode 100644 combine_data_Catboost_multiple_timepoints.py create mode 100644 connectome_lead_mapper_compute.py create mode 100644 connectome_lead_mapper_write_data.py create mode 100644 figure_29_LOSO_LOHO_ind.py create mode 100644 figure_30_RMAP_vs_no_RMAP.py create mode 100644 figure_31_how_much_data_is_needed.py create mode 100644 how_much_data_is_best_for_CatBoost.py create mode 100644 how_much_data_is_needed_bk.py create mode 100644 how_much_data_is_needed_tremor.py delete mode 100644 rmap_corr.csv create mode 100644 uv.lock create mode 100644 write_mask_connectivity.m diff --git a/combine_data_Catboost_multiple_timepoints.py b/combine_data_Catboost_multiple_timepoints.py new file mode 100644 index 00000000..7783df7b --- /dev/null +++ b/combine_data_Catboost_multiple_timepoints.py @@ -0,0 +1,74 @@ +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +import os + + +if __name__ == "__main__": + PATH_FIGURES = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/figures_ucsf" + PATH_READ = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized_10s_window_length/480" + df_all = pd.read_csv(os.path.join(PATH_READ, "all_merged_normed.csv"), index_col=0) + df_all["pkg_dt"] = pd.to_datetime(df_all["pkg_dt"]) + subs = df_all["sub"].unique() + + def running_counter(values): + counter = 0 + result = [] + for value in values: + if value == 0: + counter = 0 # Reset counter on zero + else: + counter += 1 + result.append(counter) + return result + + df_all["time_diff"] = df_all["pkg_dt"].diff().dt.total_seconds() / 60 + df_all["counter"] = 0 + df_all.loc[df_all["time_diff"] == 2, "counter"] = 1 + + df_all["cum_sum"] = running_counter(df_all["counter"].values) + # fileter from df_all only the columns that start with pkg, cum_sum or that contain fft + df_all_filter = df_all[[c for c in df_all.columns if c.startswith("pkg") or c.startswith("cum_sum") or "fft" in c or "sub" == c]] + + for consec_duration in [2, 5, 10, 20, 50, 100, 200, 500]: + idx_larger_ = np.where(df_all["cum_sum"] >= consec_duration) + # for each index larger, extract the previous conec_duration dataframe and append it to a list + l_df = [] + for idx in idx_larger_[0]: + df_ = df_all_filter.iloc[idx-consec_duration:idx] + # for each row, add the rows as columns with "lag_" prefix + l_row = [] + for i in range(consec_duration): + if i > 0: + df_ = df_.rename(columns={c: f"lag_{i}_{c}" for c in df_.columns}) + l_row.append(df_.iloc[i]) + else: + l_row.append(df_.iloc[i]) + l_df.append(pd.concat(l_row, axis=0)) + df_duration = pd.DataFrame(l_df) + print(f"Duration: {consec_duration}") + print(df_duration.shape) + df_duration.to_csv(os.path.join(PATH_READ, f"all_merged_normed_merge_{consec_duration}_consec.csv")) + + + + PLT_ = False + if PLT_: + # set a column cum_sum to count counter iteratively, but reset when counter is 0 + plt.hist(df_all["cum_sum"]*2, bins=50, density=False) + plt.xlabel("Minutes") + plt.title("Histogram of time series lengths") + plt.savefig(os.path.join(PATH_FIGURES, "histogram_time_series_lengths.pdf")) + plt.show(block=True) + + # make the upper plot as a cdf + plt.hist(df_all["cum_sum"]*2, bins=50, density=True, cumulative=True, histtype='step') + # flip the y axis + # make the y axis percentage + plt.xlabel("Minutes") + # make x axis log + plt.xlim(0, 610) + # put for each tick a label + plt.title("CDF of time series lengths") + #plt.savefig(os.path.join(PATH_FIGURES, "cdf_time_series_lengths.pdf")) + plt.show(block=True) diff --git a/connectome_lead_mapper_compute.py b/connectome_lead_mapper_compute.py new file mode 100644 index 00000000..0f021eb9 --- /dev/null +++ b/connectome_lead_mapper_compute.py @@ -0,0 +1,105 @@ +import pickle +import pandas as pd +import numpy as np +from py_neuromodulation import nm_RMAP, nm_stats +from matplotlib import pyplot as plt +import os +import nibabel as nib +import seaborn as sns + +PATH_CONNECTIVITY = r"/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/out_connectivity/out_conn" +PATH_PER = r'/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/out_dir/df_per_ind_all_coords.csv' + +PLT_ = False + +df_per = pd.read_csv(PATH_PER, index_col=0) +l_ch_sel = [] + +for class_idx, CLASSIFICATION in enumerate([True, False]): + plt.figure(figsize=(10, 10)) + plt_idx = 0 + for label_idx, label_name in enumerate(["pkg_dk", "pkg_bk", "pkg_tremor"]): + for loc_idx, loc in enumerate(["ECOG", "STN", "GP"]): + df_disease = df_per.query(f"classification == {CLASSIFICATION} and label == '{label_name}' and loc == '{loc}'") + + subs = np.unique([sub[:-1] for sub in df_disease["sub"].unique()]) + df_disease["sub_ind"] = df_disease["sub"].apply(lambda x: x[:-1]) + df_disease["hemisphere"] = df_disease["sub"].apply(lambda x: x[-1]) + per_test_ = [] + pred_corr_test = [] + + + for sub_test in df_disease["sub_ind"].unique(): + l_fp_train = [] + per_train = [] + l_fp_test = [] + per_test = [] + chs_test = [] + hemispheres_test = [] + + affine = None + + for idx, row in df_disease.iterrows(): + f_name = f'{row["sub"]}_ROI_{row["ch_orig"]}_conn-PPMI74P15CxPatients_desc-AvgRFz_funcmap.nii' + f_path = os.path.join(PATH_CONNECTIVITY, f_name) + rmapsel = nm_RMAP.RMAPCross_Val_ChannelSelector() + fp = rmapsel.load_fingerprint(f_path) + if affine is None: + affine = rmapsel.affine + + if row["sub_ind"] == sub_test: + l_fp_test.append(fp) + per_test.append(row["per"]) + chs_test.append(row["ch_orig"]) + hemispheres_test.append(row["hemisphere"]) + else: + l_fp_train.append(fp) + per_train.append(row["per"]) + + rmap = nm_RMAP.RMAPCross_Val_ChannelSelector().get_RMAP(np.array(l_fp_train).T, np.array(per_train)) + + #nm_RMAP.RMAPCross_Val_ChannelSelector().save_Nii(rmap, affine, os.path.join(PATH_CONNECTIVITY, f"rmap_func_{label_name}_class_{CLASSIFICATION}_loc_{loc}.nii")) + corr_test = [np.corrcoef( + np.nan_to_num(rmap.flatten()), + np.nan_to_num(fp.flatten()) + )[0][1] for fp in l_fp_test] + per_test_.append(per_test) + pred_corr_test.append(corr_test) + + df_test_pr = pd.DataFrame({"per": per_test, "corr": corr_test, "hemisphere": hemispheres_test, "ch": chs_test}) + + for hem in df_test_pr["hemisphere"].unique(): + df_test_sub = df_test_pr.query(f"hemisphere == '{hem}'") + ch_sel = df_test_sub["ch"].values[np.argmax(df_test_sub["corr"].values)] + l_ch_sel.append({ + "label": label_name, + "CLASSIFICATION": CLASSIFICATION, + "loc": loc, + "sub": sub_test, + "ch": ch_sel, + "hemisphere": hem, + "corr": np.max(df_test_sub["corr"].values), + "per": df_test_sub.query(f"ch == '{ch_sel}'")["per"].values[0] + }) + #chs_test[np.argmax(corr_test)] + + plt_idx += 1 + if PLT_: + plt.subplot(3, 3, plt_idx) + df_plt = pd.DataFrame({"per": np.concatenate(per_test_), "corr": np.concatenate(pred_corr_test)}) + sns.regplot(data=df_plt, x="corr", y="per") + corr___ = np.corrcoef(np.concatenate(per_test_), np.concatenate(pred_corr_test))[0, 1] + print(corr___) + _, p = nm_stats.permutationTestSpearmansRho(np.concatenate(per_test_), np.concatenate(pred_corr_test), False, None, 1000) + plt.title(f"{label_name} {loc}\n" + + f"cor = {np.round(corr___, 2)} p={np.round(p, 3)}" + ) + if PLT_: + plt.suptitle(f"CLASS: {CLASSIFICATION}") + plt.tight_layout() + plt.show(block=True) +print("ha") +df_ch_sel = pd.DataFrame(l_ch_sel) +df_ch_sel.to_csv(os.path.join(PATH_CONNECTIVITY, "df_ch_sel_RMAP.csv")) + +# df_ch_sel.query("CLASSIFICATION == True and loc == 'STN' and label == 'pkg_dk'").sort_values("sub") \ No newline at end of file diff --git a/connectome_lead_mapper_write_data.py b/connectome_lead_mapper_write_data.py new file mode 100644 index 00000000..c69d6b77 --- /dev/null +++ b/connectome_lead_mapper_write_data.py @@ -0,0 +1,122 @@ +import pandas as pd +import numpy as np +import os + +PATH_RMAP_CH = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/out_connectivity/out_conn/df_ch_sel_RMAP.csv' +PATH_DATA = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_std_10s_window_length_all_ch' +PATH_OUT = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap' + +cortex_ch_names = ['8-9', '8-10', '10-11', '9-11', '8-11', '9-10'] +subcortex_ch_names = ['0-2', '0-1', '1-2', '1-3', '2-3', '0-3'] + +def get_most_recorded_chs(df_all): + # get two most recorded cortex channels + chs_available = [f[:f.find("_raw_")] for f in df_all.columns if "raw_" in f and "mean" in f] + size_ = [] + for ch in cortex_ch_names: + if ch in chs_available: + dat_ = df_all[f"{ch}_raw_mean"] + # count the number of non-NaN values + size_.append(dat_.dropna().shape[0]) + else: + size_.append(0) + # get the names of the two most recorded channels + ch_cortex_sel = [cortex_ch_names[i] for i in np.argsort(size_)[::-1][:2]] + ch_cortex_sel = sorted(ch_cortex_sel, key=lambda x: int(x.split('-')[0])) + + size_ = [] + for ch in subcortex_ch_names: + if ch in chs_available: + dat_ = df_all[f"{ch}_raw_mean"] + # count the number of non-NaN values + size_.append(dat_.dropna().shape[0]) + else: + size_.append(0) + # get the names of the two most recorded channels + ch_subcortex_sel = [subcortex_ch_names[i] for i in np.argsort(size_)[::-1][:2]] + ch_subcortex_sel = sorted(ch_subcortex_sel, key=lambda x: int(x.split('-')[0])) + return ch_cortex_sel, ch_subcortex_sel + +df_rmap_ch = pd.read_csv(PATH_RMAP_CH) + +# compute the performance 1. renaming the columns + +SELECT_ALL = False +subs = df_rmap_ch["sub"].unique() + +if SELECT_ALL: + + dfs_save = [] + cols = None + for sub in subs: + print(sub) + for hem in ["l", "r"]: + if not os.path.exists(os.path.join(PATH_DATA, f"{sub}{hem}_merged.csv")): + continue + df_sub = pd.read_csv(os.path.join(PATH_DATA, f"{sub}{hem}_merged.csv")) + ch_cortex, ch_subcortex = get_most_recorded_chs(df_sub) + ch_used = ch_subcortex + ch_cortex + # select all columns of df_sub that contain the ch_used + col_used = [c for c in df_sub.columns if any([ch in c for ch in ch_used]) and "coh" not in c] + list(df_sub.columns[-4:]) + df_sub_used = df_sub[col_used] + df_sub_orig = df_sub_used.copy() + ch_names_replace = ["ch_subcortex_1", "ch_subcortex_2", "ch_cortex_1", "ch_cortex_2"] + + # for each column replace the ch_used with the ch_names_replace + for ch, ch_name in zip(ch_used, ch_names_replace): + df_sub_used.columns = [c.replace(ch+"_", ch_name+"_") for c in df_sub_used.columns] + if cols is None: + cols = df_sub_used.columns + else: + if not np.all(cols == df_sub_used.columns): + df_sub_used = df_sub_used[cols] + + df_sub_used["sub"] = sub+hem + dfs_save.append(df_sub_used) + print(df_sub_used.columns.shape) + if df_sub_used.columns.shape[0] != 2725: + print("break here") + # df_sub_used can be saved + + df_all = pd.concat(dfs_save) + df_all.to_csv(os.path.join(PATH_OUT, "all_ch_renamed_no_rmap.csv")) + + +for CLASSIFICATION in [True, False]: + print(CLASSIFICATION) + for label in ["pkg_dk", "pkg_bk", "pkg_tremor"]: + print(label) + df_l = [] + cols = None + for sub in subs: + print(sub) + for hem in ["l", "r"]: + if not os.path.exists(os.path.join(PATH_DATA, f"{sub}{hem}_merged.csv")): + continue + df_sub = pd.read_csv(os.path.join(PATH_DATA, f"{sub}{hem}_merged.csv")) + + ch_sel = list(df_rmap_ch.query(f"sub == '{sub}' and hemisphere == '{hem}' and label == '{label}' and CLASSIFICATION == {CLASSIFICATION}")["ch"]) + # sort the channels + ch_sel = sorted(ch_sel, key=lambda x: int(x.split('-')[0])) + # select only columns that start with one ch in ch_sel + col_used = [c for c in df_sub.columns if any([ch in c for ch in ch_sel]) and "coh" not in c] + list(df_sub.columns[-4:]) + df_sub_sel = df_sub[col_used] + ch_replace = ["ch_subcortex_1", "ch_cortex_1"] + for ch, ch_name in zip(ch_sel, ch_replace): + df_sub_sel.columns = [c.replace(ch+"_", ch_name+"_") for c in df_sub_sel.columns] + if cols is None: + cols = df_sub_sel.columns + else: + if len(cols) != len(df_sub_sel.columns): + continue + if not np.all(cols == df_sub_sel.columns): + try: + df_sub_sel = df_sub_sel[cols] + except: + continue + df_sub_sel["sub"] = sub+hem + df_l.append(df_sub_sel) + df_rmap_cond = pd.concat(df_l) + # remove entries where the label is NaN + df_rmap_cond = df_rmap_cond[~df_rmap_cond[label].isna()] + df_rmap_cond.to_csv(os.path.join(PATH_OUT, f"rmap_ch_{label}_class_{CLASSIFICATION}.csv")) diff --git a/figure_27_get_ch_ind_per_all_ch.py b/figure_27_get_ch_ind_per_all_ch.py index 3e3477e7..809ce65e 100644 --- a/figure_27_get_ch_ind_per_all_ch.py +++ b/figure_27_get_ch_ind_per_all_ch.py @@ -42,12 +42,15 @@ def run_channel(sub, ch, ch_idx): X = X[~idx_nan] y = y[np.array(~idx_nan)] - y_pr = model_selection.cross_val_predict( - model, - X, - y, - cv=model_selection.KFold(n_splits=3, shuffle=False), - ) + try: + y_pr = model_selection.cross_val_predict( + model, + X, + y, + cv=model_selection.KFold(n_splits=3, shuffle=False), + ) + except: + continue if CLASSIFICATION: diff --git a/figure_29_LOSO_LOHO_ind.py b/figure_29_LOSO_LOHO_ind.py new file mode 100644 index 00000000..55d7752f --- /dev/null +++ b/figure_29_LOSO_LOHO_ind.py @@ -0,0 +1,77 @@ +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +import pickle +import seaborn as sns +import os + +PATH_PER = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per" +PATH_FIGURES = r"/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/figures_ucsf" + +# read f"ind_subjects_cvall_all.pkl" + +with open(f"{PATH_PER}/ind_subjects_cvall_all.pkl", "rb") as f: + d_out = pickle.load(f) + +l = [] +for CLASSFICATION in d_out.keys(): + for pkg_label in d_out[CLASSFICATION].keys(): + for sub in d_out[CLASSFICATION][pkg_label].keys(): + l.append({ + "sub": sub, + "pkg_label": pkg_label, + "CLASSIFICATION": CLASSFICATION, + "per": d_out[CLASSFICATION][pkg_label][sub]["per"] + }) +df_loso = pd.DataFrame(l) +df_loso["cv"] = "LOSO" + + +PATH_PRE = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/LOSO_ALL_LABELS_ALL_GROUPS.pkl' +with open(PATH_PRE, "rb") as f: + d_out = pickle.load(f) + +l = [] +for CLASSIFICATION in d_out.keys(): + for pkg_label in d_out[CLASSIFICATION].keys(): + for sub in d_out[CLASSIFICATION][pkg_label]["ecog_stn"].keys(): + if CLASSIFICATION is True: + per_ = "ba" + else: + per_ = "corr_coeff" + l.append({ + "sub": sub, + "pkg_label": pkg_label, + "CLASSIFICATION": CLASSIFICATION, + "per": d_out[CLASSIFICATION][pkg_label]["ecog_stn"][sub][per_] + }) +df_loho = pd.DataFrame(l) +df_loho["cv"] = "LOHO" + +# read individual performances +PATH_PER = r"/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/out_dir" +df_ind = pd.read_csv(os.path.join(PATH_PER, "df_per_ind_all_coords.csv"), index_col=0) +# rename 'CLASSIFICATION' to 'classification' +df_ind = df_ind.rename(columns={"classification": "CLASSIFICATION"}) +df_ind = df_ind.rename(columns={"label": "pkg_label"}) +df_ind = df_ind.groupby(["CLASSIFICATION", "pkg_label", "sub"]).max().reset_index() +df_ind["cv"] = "ind" + +df = pd.concat([df_loso, df_loho, df_ind], axis=0) + +plt.figure(figsize=(10, 5), dpi=300) +sns.boxplot(x="pkg_label", y="per", hue="cv", data=df.query("CLASSIFICATION == False"), showfliers=False, showmeans=True, palette="viridis") +sns.swarmplot(x="pkg_label", y="per", hue="cv", data=df.query("CLASSIFICATION == False"), alpha=0.5, dodge=True, size=2.5, palette="viridis") +plt.ylabel("Correlation coefficient") +plt.tight_layout() +plt.savefig(os.path.join(PATH_FIGURES, "figure_29_LOSO_LOHO_ind_CLASS_False.pdf")) +plt.show(block=True) + +plt.figure(figsize=(10, 5), dpi=300) +sns.boxplot(x="pkg_label", y="per", hue="cv", data=df.query("CLASSIFICATION == True"), showfliers=False, showmeans=True, palette="viridis") +sns.swarmplot(x="pkg_label", y="per", hue="cv", data=df.query("CLASSIFICATION == True"), alpha=0.5, dodge=True, size=2.5, palette="viridis") +plt.ylabel("Balanced accuracy") +plt.tight_layout() +plt.savefig(os.path.join(PATH_FIGURES, "figure_29_LOSO_LOHO_ind_CLASS_True.pdf")) +plt.show(block=True) + diff --git a/figure_30_RMAP_vs_no_RMAP.py b/figure_30_RMAP_vs_no_RMAP.py new file mode 100644 index 00000000..b82d19e3 --- /dev/null +++ b/figure_30_RMAP_vs_no_RMAP.py @@ -0,0 +1,85 @@ +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +import pickle +import os +import seaborn as sns + + +# previous result +PATH_PRE = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/LOSO_ALL_LABELS_ALL_GROUPS.pkl' +PATH_NOW = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap/normed/480/all_ch_renamed_no_rmap/LOHO_ALL_LABELS_ALL_GROUPS.pkl' +PATH_NOW = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap/normed/480/rmap_ch_pkg_dk_class_True/LOHO_ALL_LABELS_ALL_GROUPS.pkl' +with open(PATH_NOW, "rb") as f: + d_out = pickle.load(f) + +l = [] +for CLASSIFICATION in d_out.keys(): + for pkg_label in d_out[CLASSIFICATION].keys(): + for sub in d_out[CLASSIFICATION][pkg_label]["ecog_stn"].keys(): + if CLASSIFICATION is True: + per_ = "ba" + else: + per_ = "corr_coeff" + l.append({ + "sub": sub, + "pkg_label": pkg_label, + "CLASSIFICATION": CLASSIFICATION, + "per": d_out[CLASSIFICATION][pkg_label]["ecog_stn"][sub][per_] + }) +df_loso = pd.DataFrame(l) +df_loso.groupby(["CLASSIFICATION", "pkg_label"])["per"].mean() + +def read_per(d_out): + l = [] + for CLASSIFICATION in d_out.keys(): + for pkg_label in d_out[CLASSIFICATION].keys(): + for sub in d_out[CLASSIFICATION][pkg_label].keys(): + l.append({ + "sub": sub, + "pkg_label": pkg_label, + "CLASSIFICATION": CLASSIFICATION, + "per": d_out[CLASSIFICATION][pkg_label][sub]["per"] + }) + df_loso = pd.DataFrame(l) + return df_loso + +PATH_PER = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap/normed/480' +folders = [f for f in os.listdir(PATH_PER) if os.path.isdir(os.path.join(PATH_PER, f))] + +l_ = [] +for folder in folders: + with open(os.path.join(PATH_PER, folder, "loso_per.pkl"), "rb") as f: + d_out = pickle.load(f) + d_per = read_per(d_out) + if "no_rmap" in folder: + d_per["rmap"] = False + else: + d_per["rmap"] = True + l_.append(d_per) + +df = pd.concat(l_, axis=0) + +# plot the performances for each condition, CLASSIFICATION, pkg_label and RMAP, RMAP is hue +# one subplot for each CLASSIFICATION +meanprops = {"marker": "o", "markerfacecolor": "red", "markeredgecolor": "red"} + +plt.figure() +plt.subplot(1, 2, 1) +sns.boxplot(data=df.query("CLASSIFICATION == True"), x="pkg_label", y="per", hue="rmap", showmeans=True, palette="viridis", meanprops=meanprops) +sns.swarmplot(data=df.query("CLASSIFICATION == True"), x="pkg_label", y="per", hue="rmap", dodge=True, palette="viridis") +plt.ylabel("Balanced accuracy") +plt.subplot(1, 2, 2) +sns.boxplot(data=df.query("CLASSIFICATION == False"), x="pkg_label", y="per", hue="rmap", showmeans=True, palette="viridis", meanprops=meanprops) +sns.swarmplot(data=df.query("CLASSIFICATION == False"), x="pkg_label", y="per", hue="rmap", dodge=True, palette="viridis") +plt.ylabel("Correlation coefficient") +plt.show(block=True) + +# print the mean and std for each condition +print(df.groupby(["CLASSIFICATION", "pkg_label", "rmap"])["per"].mean()) + +# clip per for classifciation to 0.5 and 1 and calculate mean and std +df["per"] = df["per"].clip(0.5, 1) +print(df.groupby(["CLASSIFICATION", "pkg_label", "rmap"])["per"].mean()) + + diff --git a/figure_31_how_much_data_is_needed.py b/figure_31_how_much_data_is_needed.py new file mode 100644 index 00000000..b3e013f9 --- /dev/null +++ b/figure_31_how_much_data_is_needed.py @@ -0,0 +1,75 @@ +import os +import pandas as pd +import numpy as np +import seaborn as sns +from matplotlib import pyplot as plt +import pickle + +PATH_PER = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per' +PATH_FIGURE = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/figures_ucsf' + +for label in ["tremor", "DK", "bk"]: + files = [f for f in os.listdir(PATH_PER) if "LOHO" in f and"_min.pkl" in f and "_"+label+"_" in f] + df_ = [] + for f in files: + with open(os.path.join(PATH_PER, f), "rb") as f: + d_out = pickle.load(f) + + l = [] + for CLASS in d_out.keys(): + for label_name in d_out[CLASS].keys(): + for loc_ in d_out[CLASS][label_name].keys(): + for sub_test in d_out[CLASS][label_name][loc_].keys(): + if label == "DK" or label == "tremor": + per_label = "ba" + else: + per_label = "corr_coeff" + l.append({ + "sub": sub_test, + "pkg_label": label_name, + "CLASS": CLASS, + "per": d_out[CLASS][label_name][loc_][sub_test][per_label] + }) + df = pd.DataFrame(l) + df["dur"] = int(str(f).split("_")[-2]) + + df_.append(df) + + df = pd.concat(df_, axis=0) + # clip the balanced accuracy to 0.5 and 1 + df["per"] = np.clip(df["per"], 0.5, 1) + + df.groupby("dur")["per"].mean() + + # make a line plot for each subject and each duration + plt.figure(figsize=(10, 5), dpi=300) + durations = np.sort(df["dur"].unique()) + sub_per = [] + for sub in df["sub"].unique(): + df_sub = df.query(f"sub == '{sub}'") + # sort the dataframe by duration + df_sub = df_sub.sort_values("dur") + plt.plot(durations / 60, df_sub["per"], color="gray", alpha=0.2) + sub_per.append(df_sub["per"].values) + plt.xlabel("Duration [h]") + plt.ylabel("Balanced accuracy") + # plot the mean accuracy for each duration + plt.plot(durations / 60, np.array(sub_per).mean(axis=0), marker="o", linestyle="-", color="black") + plt.xscale('log') + plt.title("LOHO PKG DK CLASS CV different training duration") + plt.tight_layout() + plt.savefig(os.path.join(PATH_FIGURE, f"LOHO_different_training_duration_sub_{label}.pdf")) + plt.show(block=True) + +plt.figure(figsize=(10, 5)) +sns.boxplot(data=df, x="dur", y="per", palette="viridis", showfliers=False, showmeans=True) +sns.swarmplot(data=df, x="dur", y="per", palette="viridis", alpha=0.5, dodge=False, size=2.5) +plt.xlabel("Duration [min]") +plt.ylabel("Balanced accuracy") +plt.title("LOHO PKG DK CLASS CV different training duration") +# write the mean accuracy on top of the boxplot +for i, dur in enumerate(np.sort(df["dur"].unique())): + plt.text(i, df.query(f"dur == {dur}")["per"].mean(), f"{np.round(df.query(f'dur == {dur}')['per'].mean(), 2)}", ha="center", va="bottom") +plt.savefig(os.path.join(PATH_FIGURE, "LOHO_different_training_duration.pdf"), dpi=300) +plt.show(block=True) + diff --git a/how_much_data_is_best_for_CatBoost.py b/how_much_data_is_best_for_CatBoost.py new file mode 100644 index 00000000..8a44493b --- /dev/null +++ b/how_much_data_is_best_for_CatBoost.py @@ -0,0 +1,159 @@ +import os +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +from sklearn import linear_model, metrics, model_selection, ensemble +from catboost import CatBoostRegressor, Pool, CatBoostClassifier +from sklearn.utils.class_weight import compute_class_weight +from sklearn.model_selection import StratifiedShuffleSplit + +from sklearn.utils import shuffle +import pickle + +PATH_READ = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized_10s_window_length/480" +PATH_PER = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per" + +#PATH_PER = "/data/cephfs-1/home/users/merkt_c/work/UCSF_Analysis/out_per" +#PATH_READ = "/data/cephfs-1/home/users/merkt_c/work/UCSF_Analysis" + +def compute_duration(dur, CLASS, label_name): + + # all_merged_normed_merge_5_consec + df_all = pd.read_csv(os.path.join(PATH_READ, f"all_merged_normed_merge_{dur}_consec.csv"), index_col=0) + df_all["pkg_dt"] = pd.to_datetime(df_all["pkg_dt"]) + subs = df_all["sub"].unique() + #dur_l = [df_all.query(f"sub == '{sub}'").shape[0] for sub in subs] + + print(f"Duration: {dur}") + d_out = {} + loc_ = "ecog_stn" + d_out[CLASS] = {} + + d_out[CLASS][label_name] = {} + d_out[CLASS][label_name][loc_] = {} + + mask = ~df_all[label_name].isnull() + df_use = df_all[mask].copy() + + if label_name == "pkg_dk": + df_use[label_name] = (df_use[label_name].copy() / df_use[label_name].max()) > 0.02 + elif label_name == "pkg_tremor": + df_use[label_name] = df_use[label_name].copy() > 1 + elif label_name == "pkg_bk": + df_use[label_name] = df_use[label_name].copy() > 50 + + for sub_test in subs: # tqdm( + print(f"sub_test: {sub_test}") + + df_test = df_use[df_use["sub"] == sub_test] + + df_test = df_test.drop(columns=["sub"]) + y_test = np.array(df_test[label_name]) + df_train = df_use[df_use["sub"] != sub_test] + df_train = df_train.drop(columns=["sub"]) + y_train = np.array(df_train[label_name]) + + X_train = df_train[[c for c in df_train.columns if "pkg" not in c and "cum_sum" not in c and not c.endswith("_sub")]] + X_train["hour"] = df_train["pkg_dt"].dt.hour + + X_test = df_test[[c for c in df_test.columns if "pkg" not in c and "cum_sum" not in c and not c.endswith("_sub")]] + X_test["hour"] = df_test["pkg_dt"].dt.hour + + if X_test.shape[0] == 0: + continue + if X_train.shape[0] == 0: + continue + + #X_ = X.dropna(axis=1) # drop all columns that have NaN values + if CLASS: + classes = np.unique(y_train) + weights = compute_class_weight(class_weight='balanced', classes=classes, y=y_train) + class_weights = dict(zip(classes, weights)) + + model = CatBoostClassifier(silent=True, class_weights=class_weights) + else: + model = CatBoostRegressor(silent=True) # task_type="GPU" + + # drop columns that have NaN values + X_train = X_train.dropna(axis=1) + X_test = X_test[X_train.columns] + + # drop columns that have inf values + X_train = X_train.replace([np.inf, -np.inf], np.nan) + X_train = X_train.dropna(axis=1) + X_test = X_test[X_train.columns] + + # drop X_test columns that have inf values + X_test = X_test.replace([np.inf, -np.inf], np.nan) + X_test = X_test.dropna(axis=1) + X_train = X_train[X_test.columns] + + # which columns contain inf + # replace NaN values with 0 + X_test = X_test.fillna(0) + + # shuffle X_train and y_train + #X_train, y_train = shuffle(X_train, y_train, random_state=42) + + # if CLASS: + # n_samples_per_class = int(dur/4) + # idx_pos = np.where(y_train == 1)[0][:n_samples_per_class] + # idx_neg = np.where(y_train == 0)[0][:n_samples_per_class] + # X_train = pd.concat([X_train.iloc[idx_pos], X_train.iloc[idx_neg]]) + # y_train = np.concatenate([y_train[idx_pos], y_train[idx_neg]]) + + model.fit(X_train, y_train) + + pr = model.predict(X_test) + + feature_importances = model.get_feature_importance(Pool(X_test, y_test), type="PredictionValuesChange") + + d_out[CLASS][label_name][loc_][sub_test] = {} + if CLASS: + y_test = y_test.astype(int) + pr = pr.astype(int) + d_out[CLASS][label_name][loc_][sub_test]["accuracy"] = metrics.accuracy_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["ba"] = metrics.balanced_accuracy_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["f1"] = metrics.f1_score(y_test, pr) + #d_out[CLASS][label_name][loc_][sub_test]["pr_proba"] = model.predict_proba(X_test) + #d_out[CLASS][label_name][loc_][sub_test]["true_reg_normed"] = df_use[df_use["sub"] == sub_test][label_names_reg[label_idx]] + + else: + corr_coeff = np.corrcoef(pr, np.array(y_test))[0, 1] + d_out[CLASS][label_name][loc_][sub_test]["corr_coeff"] = corr_coeff + d_out[CLASS][label_name][loc_][sub_test]["r2"] = metrics.r2_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["mse"] = metrics.mean_squared_error(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["mae"] = metrics.mean_absolute_error(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["pr"] = pr + d_out[CLASS][label_name][loc_][sub_test]["y_"] = y_test + d_out[CLASS][label_name][loc_][sub_test]["time"] = df_test["pkg_dt"].values + d_out[CLASS][label_name][loc_][sub_test]["feature_importances"] = feature_importances + + SAVE_NAME = f"LOHO_{label_name}_cont_data_CatBOOST_{str(dur)}_min_CLASS_{CLASS}.pkl" + with open(os.path.join(PATH_PER, SAVE_NAME), "wb") as f: + pickle.dump(d_out, f) + +if __name__ == "__main__": + import sys + #idx_ = int(sys.argv[1]) + idx_ = 0 + + label_names = ["pkg_bk", "pkg_dk", "pkg_tremor"] + CLASSES = [True, False] + + comb_durations = [2, 5, 10, 20, 50, 100, 200] + + cnt = 0 + for label_idx, label in enumerate(label_names): + for CLASS_Idx, CLASS in enumerate(CLASSES): + for comb_dura_idx, dur in enumerate(comb_durations): + if cnt == idx_: + compute_duration(dur, CLASS, label) + cnt = cnt + 1 + + + #for duration in comb_durations: + # compute_duration(duration) + #compute_duration(durations[idx_]) + #from joblib import Parallel, delayed + #Parallel(n_jobs=4)(delayed(compute_duration)(dur) for dur in [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384]) \ No newline at end of file diff --git a/how_much_data_is_needed_bk.py b/how_much_data_is_needed_bk.py new file mode 100644 index 00000000..97a5e104 --- /dev/null +++ b/how_much_data_is_needed_bk.py @@ -0,0 +1,140 @@ +import os +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +from sklearn import linear_model, metrics, model_selection, ensemble +from catboost import CatBoostRegressor, Pool, CatBoostClassifier +from sklearn.utils.class_weight import compute_class_weight +from sklearn.model_selection import StratifiedShuffleSplit + +from sklearn.utils import shuffle +import pickle + +PATH_READ = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized_10s_window_length/480" +PATH_PER = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per" + +#PATH_PER = "/data/cephfs-1/home/users/merkt_c/work/UCSF_Analysis/out_per" +#PATH_READ = "/data/cephfs-1/home/users/merkt_c/work/UCSF_Analysis" + +label_name = "pkg_bk" +CLASS = False + +df_all = pd.read_csv(os.path.join(PATH_READ, "all_merged_normed.csv"), index_col=0) +df_all["pkg_dt"] = pd.to_datetime(df_all["pkg_dt"]) +subs = df_all["sub"].unique() +dur_l = [df_all.query(f"sub == '{sub}'").shape[0] for sub in subs] + +def compute_duration(dur): +#for dur in [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384]: + print(f"Duration: {dur}") + d_out = {} + loc_ = "ecog_stn" + d_out[CLASS] = {} + + d_out[CLASS][label_name] = {} + d_out[CLASS][label_name][loc_] = {} + + mask = ~df_all[label_name].isnull() + df_use = df_all[mask].copy() + + if label_name == "pkg_dk": + df_use[label_name] = (df_use[label_name].copy() / df_use[label_name].max()) > 0.02 + elif label_name == "pkg_tremor": + df_use[label_name] = df_use[label_name].copy() > 1 + + for sub_test in subs: # tqdm( + print(f"sub_test: {sub_test}") + + df_test = df_use[df_use["sub"] == sub_test] + + df_test = df_test.drop(columns=["sub"]) + y_test = np.array(df_test[label_name]) + df_train = df_use[df_use["sub"] != sub_test] + df_train = df_train.drop(columns=["sub"]) + y_train = np.array(df_train[label_name]) + + X_train = df_train[[c for c in df_train.columns if "pkg" not in c]] + X_train["hour"] = df_train["pkg_dt"].dt.hour + + X_test = df_test[[c for c in df_test.columns if "pkg" not in c]] + X_test["hour"] = df_test["pkg_dt"].dt.hour + + #X_ = X.dropna(axis=1) # drop all columns that have NaN values + if CLASS: + classes = np.unique(y_train) + weights = compute_class_weight(class_weight='balanced', classes=classes, y=y_train) + class_weights = dict(zip(classes, weights)) + + model = CatBoostClassifier(silent=True, class_weights=class_weights) + else: + model = CatBoostRegressor(silent=True) # task_type="GPU" + + # drop columns that have NaN values + X_train = X_train.dropna(axis=1) + X_test = X_test[X_train.columns] + + # drop columns that have inf values + X_train = X_train.replace([np.inf, -np.inf], np.nan) + X_train = X_train.dropna(axis=1) + X_test = X_test[X_train.columns] + + # drop X_test columns that have inf values + X_test = X_test.replace([np.inf, -np.inf], np.nan) + X_test = X_test.dropna(axis=1) + X_train = X_train[X_test.columns] + + # which columns contain inf + # replace NaN values with 0 + X_test = X_test.fillna(0) + + # shuffle X_train and y_train + X_train, y_train = shuffle(X_train, y_train, random_state=42) + + if CLASS: + n_samples_per_class = int(dur/4) + idx_pos = np.where(y_train == 1)[0][:n_samples_per_class] + idx_neg = np.where(y_train == 0)[0][:n_samples_per_class] + X_train = pd.concat([X_train.iloc[idx_pos], X_train.iloc[idx_neg]]) + y_train = np.concatenate([y_train[idx_pos], y_train[idx_neg]]) + + model.fit(X_train, y_train) + + pr = model.predict(X_test) + + feature_importances = model.get_feature_importance(Pool(X_test, y_test), type="PredictionValuesChange") + + d_out[CLASS][label_name][loc_][sub_test] = {} + if CLASS: + y_test = y_test.astype(int) + pr = pr.astype(int) + d_out[CLASS][label_name][loc_][sub_test]["accuracy"] = metrics.accuracy_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["ba"] = metrics.balanced_accuracy_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["f1"] = metrics.f1_score(y_test, pr) + #d_out[CLASS][label_name][loc_][sub_test]["pr_proba"] = model.predict_proba(X_test) + #d_out[CLASS][label_name][loc_][sub_test]["true_reg_normed"] = df_use[df_use["sub"] == sub_test][label_names_reg[label_idx]] + + else: + corr_coeff = np.corrcoef(pr, np.array(y_test))[0, 1] + d_out[CLASS][label_name][loc_][sub_test]["corr_coeff"] = corr_coeff + d_out[CLASS][label_name][loc_][sub_test]["r2"] = metrics.r2_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["mse"] = metrics.mean_squared_error(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["mae"] = metrics.mean_absolute_error(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["pr"] = pr + d_out[CLASS][label_name][loc_][sub_test]["y_"] = y_test + d_out[CLASS][label_name][loc_][sub_test]["time"] = df_test["pkg_dt"].values + d_out[CLASS][label_name][loc_][sub_test]["feature_importances"] = feature_importances + + SAVE_NAME = f"LOHO_{label_name}_{str(dur)}_min.pkl" + with open(os.path.join(PATH_PER, SAVE_NAME), "wb") as f: + pickle.dump(d_out, f) + +if __name__ == "__main__": + import sys + #idx_ = int(sys.argv[1]) + + durations = [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384] + for duration in durations: + compute_duration(duration) + #compute_duration(durations[idx_]) + #from joblib import Parallel, delayed + #Parallel(n_jobs=4)(delayed(compute_duration)(dur) for dur in [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384]) \ No newline at end of file diff --git a/how_much_data_is_needed_tremor.py b/how_much_data_is_needed_tremor.py new file mode 100644 index 00000000..90d06e7b --- /dev/null +++ b/how_much_data_is_needed_tremor.py @@ -0,0 +1,138 @@ +import os +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +from sklearn import linear_model, metrics, model_selection, ensemble +from catboost import CatBoostRegressor, Pool, CatBoostClassifier +from sklearn.utils.class_weight import compute_class_weight +from sklearn.model_selection import StratifiedShuffleSplit + +from sklearn.utils import shuffle +import pickle + +PATH_READ = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized_10s_window_length/480" +PATH_PER = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per" + +PATH_PER = "/data/cephfs-1/home/users/merkt_c/work/UCSF_Analysis/out_per" +PATH_READ = "/data/cephfs-1/home/users/merkt_c/work/UCSF_Analysis" + +label_name = "pkg_tremor" +CLASS = True + +df_all = pd.read_csv(os.path.join(PATH_READ, "all_merged_normed.csv"), index_col=0) +df_all["pkg_dt"] = pd.to_datetime(df_all["pkg_dt"]) +subs = df_all["sub"].unique() +dur_l = [df_all.query(f"sub == '{sub}'").shape[0] for sub in subs] + +def compute_duration(dur): +#for dur in [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384]: + print(f"Duration: {dur}") + d_out = {} + loc_ = "ecog_stn" + CLASS = True + d_out[CLASS] = {} + + d_out[CLASS][label_name] = {} + d_out[CLASS][label_name][loc_] = {} + + mask = ~df_all[label_name].isnull() + df_use = df_all[mask].copy() + + if label_name == "pkg_dk": + df_use[label_name] = (df_use[label_name].copy() / df_use[label_name].max()) > 0.02 + elif label_name == "pkg_tremor": + df_use[label_name] = df_use[label_name].copy() > 1 + + for sub_test in subs: # tqdm( + print(f"sub_test: {sub_test}") + + df_test = df_use[df_use["sub"] == sub_test] + + df_test = df_test.drop(columns=["sub"]) + y_test = np.array(df_test[label_name]) + df_train = df_use[df_use["sub"] != sub_test] + df_train = df_train.drop(columns=["sub"]) + y_train = np.array(df_train[label_name]) + + X_train = df_train[[c for c in df_train.columns if "pkg" not in c]] + X_train["hour"] = df_train["pkg_dt"].dt.hour + + X_test = df_test[[c for c in df_test.columns if "pkg" not in c]] + X_test["hour"] = df_test["pkg_dt"].dt.hour + + #X_ = X.dropna(axis=1) # drop all columns that have NaN values + if CLASS: + classes = np.unique(y_train) + weights = compute_class_weight(class_weight='balanced', classes=classes, y=y_train) + class_weights = dict(zip(classes, weights)) + + model = CatBoostClassifier(silent=True, class_weights=class_weights) + else: + model = CatBoostRegressor(silent=True) # task_type="GPU" + + # drop columns that have NaN values + X_train = X_train.dropna(axis=1) + X_test = X_test[X_train.columns] + + # drop columns that have inf values + X_train = X_train.replace([np.inf, -np.inf], np.nan) + X_train = X_train.dropna(axis=1) + X_test = X_test[X_train.columns] + + # drop X_test columns that have inf values + X_test = X_test.replace([np.inf, -np.inf], np.nan) + X_test = X_test.dropna(axis=1) + X_train = X_train[X_test.columns] + + # which columns contain inf + # replace NaN values with 0 + X_test = X_test.fillna(0) + + # shuffle X_train and y_train + X_train, y_train = shuffle(X_train, y_train, random_state=42) + + n_samples_per_class = int(dur/4) + idx_pos = np.where(y_train == 1)[0][:n_samples_per_class] + idx_neg = np.where(y_train == 0)[0][:n_samples_per_class] + X_train = pd.concat([X_train.iloc[idx_pos], X_train.iloc[idx_neg]]) + y_train = np.concatenate([y_train[idx_pos], y_train[idx_neg]]) + + model.fit(X_train, y_train) + + pr = model.predict(X_test) + + feature_importances = model.get_feature_importance(Pool(X_test, y_test), type="PredictionValuesChange") + + d_out[CLASS][label_name][loc_][sub_test] = {} + if CLASS: + y_test = y_test.astype(int) + pr = pr.astype(int) + d_out[CLASS][label_name][loc_][sub_test]["accuracy"] = metrics.accuracy_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["ba"] = metrics.balanced_accuracy_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["f1"] = metrics.f1_score(y_test, pr) + #d_out[CLASS][label_name][loc_][sub_test]["pr_proba"] = model.predict_proba(X_test) + #d_out[CLASS][label_name][loc_][sub_test]["true_reg_normed"] = df_use[df_use["sub"] == sub_test][label_names_reg[label_idx]] + + else: + corr_coeff = np.corrcoef(pr, np.array(y_test))[0, 1] + d_out[CLASS][label_name][loc_][sub_test]["corr_coeff"] = corr_coeff + d_out[CLASS][label_name][loc_][sub_test]["r2"] = metrics.r2_score(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["mse"] = metrics.mean_squared_error(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["mae"] = metrics.mean_absolute_error(y_test, pr) + d_out[CLASS][label_name][loc_][sub_test]["pr"] = pr + d_out[CLASS][label_name][loc_][sub_test]["y_"] = y_test + d_out[CLASS][label_name][loc_][sub_test]["time"] = df_test["pkg_dt"].values + d_out[CLASS][label_name][loc_][sub_test]["feature_importances"] = feature_importances + + SAVE_NAME = f"LOHO_{label_name}_{str(dur)}_min.pkl" + with open(os.path.join(PATH_PER, SAVE_NAME), "wb") as f: + pickle.dump(d_out, f) + +if __name__ == "__main__": + import sys + idx_ = int(sys.argv[1]) + durations = [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384] + + compute_duration(durations[idx_]) + #from joblib import Parallel, delayed + #Parallel(n_jobs=4)(delayed(compute_duration)(dur) for dur in [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384]) \ No newline at end of file diff --git a/leave_one_subject_out_not_hermisphere_validation.py b/leave_one_subject_out_not_hermisphere_validation.py index 20ae1a05..e623c2e7 100644 --- a/leave_one_subject_out_not_hermisphere_validation.py +++ b/leave_one_subject_out_not_hermisphere_validation.py @@ -9,7 +9,7 @@ from sklearn.utils.class_weight import compute_class_weight PATH_READ = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized_10s_window_length/480" -PATH_OUT = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per" +#PATH_OUT = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per" #PATH_READ = r"C:\Users\ICN_GPU\Downloads" #PATH_OUT = r"C:\Users\ICN_GPU\Downloads\out" @@ -18,14 +18,15 @@ subs_no_dyk = ["rcs10", "rcs14", "rcs15", "rcs19"] -def get_per(): +def get_per(PATH_READ, PATH_OUT, label_names=None, class_classes = [True, False],): time_start = time.time() # drop all columns that contain "psd" # df_all = df_all[[c for c in df_all.columns if "psd" not in c]] # df_all = df_all.drop(columns=["Unnamed: 0"]) df_all_orig = pd.read_csv( - os.path.join(PATH_READ, "all_merged_normed.csv"), index_col=0 + #os.path.join(PATH_READ, "all_merged_normed.csv"), index_col=0 + os.path.join(PATH_READ), index_col=0 ) subs = df_all_orig["sub"].unique() @@ -33,14 +34,14 @@ def get_per(): subs = [s for s in subs if not any([s_no for s_no in subs_no_dyk if s_no in s])] d_out = {} - - for CLASSIFICATION in [True, False]: + + for CLASSIFICATION in class_classes: print(f"CLASSIFICATION: {CLASSIFICATION}") d_out[CLASSIFICATION] = {} #if CLASSIFICATION: # label_names = ["pkg_dk_class", "pkg_bk_class", "pkg_tremor_class"] #else: - label_names = ["pkg_dk", "pkg_bk", "pkg_tremor"] + #label_names = ["pkg_dk", "pkg_bk", "pkg_tremor"] for label_name in label_names: print(f"label_name: {label_name}") @@ -138,11 +139,37 @@ def get_per(): d_out[CLASSIFICATION][label_name][hemisphere_sub]["feature_importances"] = feature_importances - with open(os.path.join(PATH_OUT, f"ind_subjects_cvall_all.pkl"), "wb") as f: - pickle.dump(d_out, f) - time_end = time.time() - print(f"Time elapsed: {time_end - time_start}") + #with open(os.path.join(PATH_OUT, f"ind_subjects_cvall_all.pkl"), "wb") as f: + with open(PATH_OUT, "wb") as f: + pickle.dump(d_out, f) + time_end = time.time() + print(f"Time elapsed: {time_end - time_start}") if __name__ == "__main__": - print(get_per()) + # get the LOSO performance for + PATH_ = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap/normed/480' + PATH_OUT_BASE = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per" + folders = os.listdir(PATH_) + for folder in folders: + if os.path.isdir(os.path.join(PATH_, folder)): + PATH_READ = os.path.join(PATH_, folder, "all_merged_normed_rmap.csv") + PATH_OUT = os.path.join(PATH_, folder, "loso_per.pkl") + + if "no_rmap" in folder: + label_names = ["pkg_dk", "pkg_bk", "pkg_tremor"] + class_classes = [True, False] + elif "pkg_bk" in folder: + label_names = ["pkg_bk"] + elif "pkg_dk" in folder: + label_names = ["pkg_dk"] + elif "pkg_tremor" in folder: + label_names = ["pkg_tremor"] + + if "class_True" in folder: + class_classes = [True] + elif "class_False" in folder: + class_classes = [False] + + print(get_per(PATH_READ, + PATH_OUT, label_names, class_classes)) diff --git a/merge_ucsf_features.py b/merge_ucsf_features.py index 60738d78..3fb2f802 100644 --- a/merge_ucsf_features.py +++ b/merge_ucsf_features.py @@ -1,8 +1,8 @@ import os import pandas as pd import numpy as np -#import matplotlib -#matplotlib.use('macosx',force=True) +import matplotlib +matplotlib.use('qt5agg',force=True) from matplotlib import pyplot as plt from sklearn import linear_model, metrics, model_selection, ensemble from scipy import stats @@ -12,9 +12,13 @@ PATH_OUT = "/Users/Timon/Documents/UCSF_Analysis/out/merged" PATH_OUT = "/Users/Timon/Documents/UCSF_Analysis/out/merged_std_10s_window_length_all_ch" +PATH_OUT = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_std_10s_window_length_all_ch" + PATH_READ = "/Users/Timon/Documents/UCSF_Analysis/out/py-neuro_out_10s_window_length" #py-neuro_out" -PATH_PKG = "/Users/Timon/Documents/UCSF_Analysis/Sandbox/pkg data" +PATH_READ = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/py-neuro_out" +PATH_PKG = "/Users/Timon/Documents/UCSF_Analysis/Sandbox/pkg data" +PATH_PKG = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/pkg_data' def merge_df_with_pkg(sub_ = "rcs02l"): l_rcs = [pd.read_csv(os.path.join(PATH_READ, sub_, f)) for f in os.listdir(os.path.join(PATH_READ, sub_)) if f"{sub_}" in f and f.endswith(".csv")] diff --git a/post_hoc_normalize_polars.py b/post_hoc_normalize_polars.py index 4ebce60e..98fd7371 100644 --- a/post_hoc_normalize_polars.py +++ b/post_hoc_normalize_polars.py @@ -5,11 +5,12 @@ import time from matplotlib import pyplot as plt -PATH_IN = "/Users/Timon/Documents/UCSF_Analysis/out/merged_std_10s_window_length_all_ch" +PATH_IN = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap" PATH_OUT_BASE = "/Users/Timon/Documents/UCSF_Analysis/out/merged_normalized_10s_window_length" +PATH_OUT_BASE = os.path.join(PATH_IN, "normed") -def process_sub(sub, normalization_window, df_all): - PATH_OUT = os.path.join(PATH_OUT_BASE, str(normalization_window)) +def process_sub(sub, normalization_window, df_all, file): + PATH_OUT = os.path.join(PATH_OUT_BASE, str(normalization_window), file) df_sub = df_all.filter(pl.col("sub") == sub) # Ensure the output directory exists @@ -76,27 +77,41 @@ def process_sub(sub, normalization_window, df_all): df_normed.write_csv(os.path.join(PATH_OUT, f"merged_normed_{sub}.csv")) if __name__ == "__main__": - df_all = pl.read_csv(os.path.join(PATH_IN, "all_merged_rmap_sel.csv")) - #df_all = df_all.with_columns(pl.col("pkg_dt").str.strptime(pl.Datetime, fmt="%Y-%m-%d %H:%M:%S")) - - df_all = df_all.with_columns(pl.Series("pkg_dt", df_all["pkg_dt"].str.to_datetime())) - df_all = df_all.sort("pkg_dt") - - subs = df_all["sub"].unique().to_list() - - # [5, 10, 20, 30, 60, 120] - # np.array([3, 5, 8, 12, 16, 20, 24])*60 - times_min = [480] - for normalization_window in times_min: - PATH_OUT = os.path.join(PATH_OUT_BASE, str(normalization_window)) - if not os.path.exists(PATH_OUT): - os.makedirs(PATH_OUT) - time_start_comp = time.time() - #process_sub(subs[0], normalization_window, df_all) - Parallel(n_jobs=-1)(delayed(process_sub)(sub, normalization_window, df_all) for sub in subs) - time_end_comp = time.time() - print(f"Time for normalization {normalization_window}: {time_end_comp - time_start_comp}") - - files = [os.path.join(PATH_OUT, f) for f in os.listdir(PATH_OUT) if "merged_normed_" in f] - df_list = [pl.read_csv(f) for f in files] - pl.concat(df_list).write_csv(os.path.join(PATH_OUT, "all_merged_normed_rmap.csv")) + for file in os.listdir(PATH_IN): + if not file.endswith(".csv"): + continue + if os.path.exists(os.path.join(PATH_OUT_BASE, str(480), file[:-4], "all_merged_normed_rmap.csv")): + continue + df_all = pl.read_csv(os.path.join(PATH_IN, file)) + + #df_all = df_all.with_columns(pl.col("pkg_dt").str.strptime(pl.Datetime, fmt="%Y-%m-%d %H:%M:%S")) + + df_all = df_all.with_columns(pl.Series("pkg_dt", df_all["pkg_dt"].str.to_datetime())) + df_all = df_all.sort("pkg_dt") + + subs = df_all["sub"].unique().to_list() + + # [5, 10, 20, 30, 60, 120] + # np.array([3, 5, 8, 12, 16, 20, 24])*60 + times_min = [480] + for normalization_window in times_min: + PATH_OUT = os.path.join(PATH_OUT_BASE, str(normalization_window), file[:-4]) + if not os.path.exists(PATH_OUT): + os.makedirs(PATH_OUT) + time_start_comp = time.time() + #process_sub(subs[0], normalization_window, df_all) + Parallel(n_jobs=-1)(delayed(process_sub)(sub, normalization_window, df_all, file[:-4]) for sub in subs) + time_end_comp = time.time() + print(f"Time for normalization {normalization_window}: {time_end_comp - time_start_comp}") + + files = [os.path.join(PATH_OUT, f) for f in os.listdir(PATH_OUT) if "merged_normed_" in f] + df_list = [pl.read_csv(f) for f in files] + + # convert every column to float except the last 11 columns of all dataframes in df_list + exclude_cols = ["pkg_dt", "sub", "pkg_dk", "pkg_bk", "pkg_tremor", "pkg_dk_normed", "pkg_bk_normed", "pkg_tremor_normed", "pkg_dk_class", "pkg_bk_class", "pkg_tremor_class"] + df_list = [ + df.with_columns([pl.col(col).cast(pl.Float64) for col in df.columns if col not in exclude_cols]) + for df in df_list + ] + + pl.concat(df_list).write_csv(os.path.join(PATH_OUT, "all_merged_normed_rmap.csv")) diff --git a/pyproject.toml b/pyproject.toml index e5bdf847..7942c1c5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -67,7 +67,6 @@ dependencies = [ "jupyter>=1.0.0", "bayesian-optimization>=1.4.3", "llvmlite>=0.43.0", - "torch>=2.2.2", ] [project.optional-dependencies] diff --git a/rmap_corr.csv b/rmap_corr.csv deleted file mode 100644 index 514e6946..00000000 --- a/rmap_corr.csv +++ /dev/null @@ -1,55 +0,0 @@ -,sub,ch,corr,per,x_mni,y_mni,z_mni -0,rcs02l,8-10,0.0017475251880281323,0.9584746199054084,-32.72343263,-22.759860075,70.97578805 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#df_all_new = pd.read_csv('/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap/normed/480/all_ch_renamed_no_rmap/all_merged_normed_rmap.csv') + #df_all = pd.read_csv('/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_rmap/normed/480/rmap_ch_pkg_dk_class_True/all_merged_normed_rmap.csv') # drop all columns that contain "psd" #df_all = df_all[[c for c in df_all.columns if "psd" not in c]] @@ -50,16 +51,16 @@ d_out = {} - for CLASS in [False, True]: + for CLASS in [True]: # [False, True] d_out[CLASS] = {} - for label_idx, label_name in enumerate(["pkg_dk", "pkg_bk", "pkg_tremor"]): + for label_idx, label_name in enumerate(["pkg_dk"]): # "pkg_bk", "pkg_tremor" d_out[CLASS][label_name] = {} mask = ~df_all[label_name].isnull() df_all = df_all[mask].copy() - for loc_ in ["ecog_stn", "ecog", "stn"]: + for loc_ in ["ecog_stn", ]: # "ecog", "stn" #if loc_ != "ecog": # continue d_out[CLASS][label_name][loc_] = {} @@ -140,30 +141,30 @@ X_train = pca.fit_transform(X_train) X_test = pca.transform(X_test) model = linear_model.LogisticRegression(class_weight="balanced") - elif MODEL_NAME == "CEBRA": - - cebra_model = CEBRA( - model_architecture = "offset1-model",#'offset40-model-4x-subsample', # previously used: offset1-model-v2' # offset10-model # my-model - batch_size = 100, - temperature_mode="auto", - learning_rate = 0.005, - max_iterations = 1000, - #time_offsets = 10, - output_dimension = 3, # check 10 for better performance - device = "mps", + # elif MODEL_NAME == "CEBRA": + + # cebra_model = CEBRA( + # model_architecture = "offset1-model",#'offset40-model-4x-subsample', # previously used: offset1-model-v2' # offset10-model # my-model + # batch_size = 100, + # temperature_mode="auto", + # learning_rate = 0.005, + # max_iterations = 1000, + # #time_offsets = 10, + # output_dimension = 3, # check 10 for better performance + # device = "mps", #conditional="time_delta", # assigning CEBRA to sample temporally and behaviorally for reference - hybrid=False, - verbose = True - ) +# hybrid=False, +# verbose = True +# ) - cebra_model.fit(X_train, y_train) - X_train_emb = cebra_model.transform(X_train) - X_test_emb = cebra_model.transform(X_test) - cebra.plot_loss(cebra_model) - cebra.plot_temperature(cebra_model) - cebra.plot_embedding(X_train_emb, cmap="viridis", markersize=10, alpha=0.5, embedding_labels=y_train.T) # embedding_labels=y_train +# cebra_model.fit(X_train, y_train) +# X_train_emb = cebra_model.transform(X_train) +# X_test_emb = cebra_model.transform(X_test) +# cebra.plot_loss(cebra_model) +# cebra.plot_temperature(cebra_model) +# cebra.plot_embedding(X_train_emb, cmap="viridis", markersize=10, alpha=0.5, embedding_labels=y_train.T) # embedding_labels=y_train - model = linear_model.LogisticRegression(class_weight="balanced") +# model = 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00000000..f8e59212 --- /dev/null +++ b/write_mask_connectivity.m @@ -0,0 +1,14 @@ +PATH_COORDS = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/out_dir/df_per_ind_all_coords.csv'; +PATH_OUT = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/out_per/out_connectivity'; + +addpath("/Users/Timon/Documents/MATLAB/spm12"); +addpath("/Users/Timon/Documents/MATLAB/leaddbs"); +addpath("/Users/Timon/Documents/MATLAB/wjn_toolbox"); + +T=readtable(PATH_COORDS); + +for a =1:size(T,1) + roiname = fullfile(PATH_OUT, strcat(string(T.sub(a)), '_ROI_', string(T.ch_orig{a}), '.nii')); + mni = [abs(T.x(a)) T.y(a) T.z(a)]; + wjn_spherical_roi(roiname,mni,4); +end \ No newline at end of file