finishing random forests

.gitignore

@@ -1,5 +1,21 @@
 data_very_raw/
 zzz_raman_spectroscopy-main/
+presentations/
+to_ignore/
+
+# ---> JupyterNotebooks
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Remove previous ipynb_checkpoints
+#   git rm -r .ipynb_checkpoints/
 
 # ---> Python
 # Byte-compiled / optimized / DLL files
@@ -161,5 +177,5 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 

classifiers/__init__.py

@@ -0,0 +1 @@
+from classifiers.evaluation import *

classifiers/evaluation.py

@@ -0,0 +1,35 @@
+from data.data_processing import process_train_test
+from sklearn.model_selection import cross_validate, ParameterGrid
+
+
+def crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=5):
+    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
+    classifier_params = {key: params[key] for key in params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
+    X_train, _ = process_train_test(process_params, experiments_train, metadata_train, scale=True)
+    clf = classifier(**classifier_params)
+    return cross_validate(clf, X_train, y_train.to_numpy().ravel(), cv=cv, return_estimator=True)
+
+
+def param_grid_search(classifier, param_grid, experiments_train, metadata_train, y_train, cv=5):
+    results = []
+    for params in ParameterGrid(param_grid):
+        try:
+            results.append([params, crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=cv)])
+            print(results[-1])
+        except Exception as e:
+            pass  # print(params, e)
+    return results
+
+
+def evaluate_classifier_params(classifier, params, X_train, y_train, X_test, y_test, iters=10):
+    train_score_mean = 0
+    test_score_mean = 0
+    for i in range(iters):
+        clf = classifier(**params)
+        clf.fit(X_train, y_train.to_numpy().ravel())
+        train_score_mean += clf.score(X_train, y_train.to_numpy().ravel())
+        test_score_mean += clf.score(X_test, y_test.to_numpy().ravel())
+    return train_score_mean / iters, test_score_mean / iters
+
+
+

data/__init__.py

@@ -0,0 +1,2 @@
+from data.data_loading import *
+from data.data_processing import *

data/data_loading.py

@@ -0,0 +1,61 @@
+import pandas as pd
+import os
+from utils.df_utils import slice_df
+
+
+def load_raw_metadata() -> pd.DataFrame:
+    """
+    Load metadata.csv
+    :return:
+    """
+    with open("../data_raw/metadata.csv") as f:
+        return pd.read_csv(f, index_col="file")
+
+
+def load_raw_measure(filename: str) -> pd.DataFrame:
+    """
+    Load a given measure session
+    :param filename:
+    :return:
+    """
+    with open(f"../data_raw/{filename}") as f:
+        df_experiment = pd.DataFrame([x.split() for x in f.readlines()])
+        df_experiment.columns = df_experiment.iloc[0]
+        df_experiment = df_experiment[1:][["#Wave", "#Intensity"]].astype(float)
+    return df_experiment
+
+
+def get_raw_measure_with_metadata(file: pd.Series) -> tuple[pd.DataFrame, list[pd.DataFrame]]:
+    """
+    Load a given measure, slice it and give the corresponding metadata
+    :param file:
+    :return: dataframe containing the metadata with one row per sliced measure
+    :return: list of sliced measure dataframes
+    """
+    df_experiment = load_raw_measure(file.name)
+    sliced_experiments = slice_df(df_experiment, df_experiment[df_experiment["#Wave"].diff() > 0].index)
+    sliced_experiments = [exp.set_index("#Wave") for exp in sliced_experiments]
+    file = pd.DataFrame(file).transpose()
+    metadata = pd.DataFrame(file.loc[file.index.repeat(len(sliced_experiments))])
+    return metadata, sliced_experiments
+
+
+def load_raw_data() -> tuple[pd.DataFrame, list[pd.DataFrame]]:
+    """
+    Load all the available data, slice it into individual measures and give the corresponding metadata
+    :return: dataframe containing the metadata with one row per sliced measure
+    :return: list of sliced measure dataframes
+    """
+    metadata, sliced_experiments = pd.DataFrame(), []
+    raw_metadata = load_raw_metadata()
+    for _, row in raw_metadata.iterrows():
+        temp_metadata, temp_sliced_experiments = get_raw_measure_with_metadata(row)
+        metadata = pd.concat([metadata, temp_metadata])
+        sliced_experiments.extend(temp_sliced_experiments)
+    return metadata, sliced_experiments
+
+
+def load_data(name: str, path: os.path = os.path.join("data")) -> tuple[pd.DataFrame, pd.DataFrame]:
+    metadata = pd.read_csv(os.path.join(path, name, "metadata.csv"))
+    experiments = pd.read_csv(os.path.join(path, name, "experiments.csv"), dtype=float)
+    return metadata, experiments

data/data_processing.py

@@ -0,0 +1,105 @@
+from pybaselines import Baseline
+from sklearn.preprocessing import StandardScaler
+from scipy.signal import savgol_filter
+import pandas as pd
+
+
+def interpolate_spectrum(exp, max_wave=1927, min_wave=181):
+    """
+    Interpolate spectra at integer wavelengths in specified wavelength range.
+    :param exp: one measure
+    :param max_wave: maximal wavelength
+    :param min_wave: minimal wavelength
+    :return: the interpolated experiment
+    """
+    result = pd.DataFrame(columns=["#Intensity"])
+    for i in range(min_wave, max_wave, 1):
+        for k in range(len(exp.index)-1):
+            if exp.index[k] == i:
+                result.loc[i] = exp["#Intensity"].iloc[k]
+                break
+            if exp.index[k] > i > exp.index[k + 1]:
+                result.loc[i] = exp["#Intensity"].iloc[k] - (
+                        exp["#Intensity"].iloc[k] - exp["#Intensity"].iloc[k + 1]) / (
+                                        exp.index[k] - exp.index[k + 1]) * (exp.index[k] - i)
+                break
+        else:
+            result.loc[i] = 0
+    return result
+
+
+def interpolate_experiments(sliced_experiments, max_wave=1927, min_wave=181):
+    result = []
+    for i, exp in enumerate(sliced_experiments):
+        result.append(interpolate_spectrum(exp, max_wave, min_wave))
+        print(i+1, "/", len(sliced_experiments))
+    return result
+
+
+def calculate_baseline(x_data, measure, lam=10, p=1e-2):
+    baseline_fitter = Baseline(x_data=x_data)
+    bkg_2, params_2 = baseline_fitter.iasls(measure, lam=lam, p=p)
+    return bkg_2
+
+
+def adjust_baseline(x_data, measure, lam=10, p=1e-2):
+    baseline = calculate_baseline(x_data, measure, lam=lam, p=p)
+    return measure - baseline
+
+
+def adjust_all_baselines(measures, lam=10, p=1e-2):
+    result = measures.copy(deep=True)
+    for index, row in result.iterrows():
+        result.iloc[index] = adjust_baseline(measures.columns.astype(int), row, lam=lam, p=p)
+    return result
+
+
+def scale_experiments(experiments):
+    result = experiments.copy(deep=True)
+    trans = StandardScaler()
+    scaled = trans.fit_transform(experiments.transpose()).T
+    result.iloc[:, :] = scaled
+    return result
+
+
+def apply_smoothing(experiment, window_length=7, polyorder=3):
+    return savgol_filter(experiment, window_length, polyorder)
+
+
+def smooth_experiments(experiments, window_length=7, polyorder=3):
+    result = experiments.copy(deep=True)
+    for index, row in result.iterrows():
+        result.iloc[index, :] = apply_smoothing(row, window_length=window_length, polyorder=polyorder)
+    return result
+
+
+def categorize_metadata(metadata: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+    truth = pd.DataFrame(pd.Categorical(metadata["strain"]).codes, index=metadata.index)
+    encoded = pd.get_dummies(data = metadata, columns = ["phase", "substrate", "confocalhigh"], dtype=int)
+    encoded.drop(columns=["replica", "strain"], inplace=True)
+    return truth, encoded
+
+
+def process_experiments(experiments: pd.DataFrame, baseline_lam: int = 10, baseline_p: float = 1e-2,
+                        smooth_window_length: int = 7, smooth_polyorder: int = 3) -> pd.DataFrame:
+    experiments = adjust_all_baselines(experiments, lam=baseline_lam, p=baseline_p)
+    experiments = scale_experiments(experiments)
+    experiments = smooth_experiments(experiments, window_length=smooth_window_length, polyorder=smooth_polyorder)
+    return experiments
+
+
+def process_train_test(params: dict, experiments_train: pd.DataFrame, metadata_train: pd.DataFrame, experiments_test: pd.DataFrame = None, metadata_test: pd.DataFrame = None, scale: bool=True) -> tuple[pd.DataFrame, pd.DataFrame]:
+    processed_train = process_experiments(experiments_train, **params)
+    X_train = pd.concat([metadata_train, processed_train], axis=1)
+    if experiments_test is not None:
+        processed_test = process_experiments(experiments_test, **params)
+        X_test = pd.concat([metadata_test, processed_test], axis=1)
+    else:
+        X_test = None
+    if scale:
+        scaler = StandardScaler()
+        scaler.fit(X_train)
+        X_train = scaler.transform(X_train)
+        if X_test is not None:
+            X_test = scaler.transform(X_test)
+    return X_train, X_test

data/test/metadata.csv

@@ -0,0 +1,178 @@
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data/train/metadata.csv

@@ -0,0 +1,529 @@
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data_raw/metadata.csv

@@ -14,16 +14,16 @@ CHA0SampleSpectraLiquid3_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh
 CHA0SampleSpectraLiquid3_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2.txt,CHA0,3.0,liquid,50,alu,100,1800,20,True,2
 CHA0SampleSpectraSolid1_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2 (2).txt,CHA0,1.0,solid,50,alu,100,1800,20,True,2
 CHA0SampleSpectraSolid1_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2.txt,CHA0,1.0,solid,50,alu,100,1800,20,True,2
-CHA0SampleSpectraSolid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu (2).txt,CHA0,,soli,50,alu,100,1800,20,True,2
-CHA0SampleSpectraSolid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu(1).txt,CHA0,,soli,50,alu,100,1800,20,True,2
-CHA0SampleSpectraSolid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu.txt,CHA0,,soli,50,alu,100,1800,20,True,2
+CHA0SampleSpectraSolid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu (2).txt,CHA0,,solid,50,alu,100,1800,20,True,2
+CHA0SampleSpectraSolid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu(1).txt,CHA0,,solid,50,alu,100,1800,20,True,2
+CHA0SampleSpectraSolid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu.txt,CHA0,,solid,50,alu,100,1800,20,True,2
 CHA0SampleSpectra_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu.txt,CHA0,,,50,alu,100,1800,20,True,2
 F113SampleSpectraLiquid3_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu (2).txt,F113,3.0,liquid,50,alu,100,1800,20,True,2
 F113SampleSpectraLiquid3_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu.txt,F113,3.0,liquid,50,alu,100,1800,20,True,2
 F113SampleSpectraLiquid3_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2_otherdrop1 (2).txt,F113,3.0,liquid,50,alu,100,1800,20,True,2
 F113SampleSpectraLiquid3_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2_otherdrop1.txt,F113,3.0,liquid,50,alu,100,1800,20,True,2
-F113SampleSpectraLiquid_50x_dried3_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2_otherdrop.txt,F113,,liqui,50,alu,100,1800,20,True,2
-F113SampleSpectraLiquid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu.txt,F113,,liqui,50,alu,100,1800,20,True,2
+F113SampleSpectraLiquid_50x_dried3_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2_otherdrop.txt,F113,,liquid,50,alu,100,1800,20,True,2
+F113SampleSpectraLiquid_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu.txt,F113,,liquid,50,alu,100,1800,20,True,2
 F113SampleSpectraSolid2_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu.txt,F113,2.0,solid,50,alu,100,1800,20,True,2
 F113SampleSpectraSolid2_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2 (2).txt,F113,2.0,solid,50,alu,100,1800,20,True,2
 F113SampleSpectraSolid2_50x_dried_drop_alu_100percent_1800gr_20sec_confocalhigh_2accu_round2.txt,F113,2.0,solid,50,alu,100,1800,20,True,2

data_raw/metadata_aggregator.py

@@ -6,7 +6,8 @@ df = pd.DataFrame([i for i in os.listdir() if i.endswith(".txt")], columns=["fil
 df["strain"] = df.apply(lambda x: x.file[:4].upper(), axis=1)
 df["replica"] = df.apply(lambda x: x.file.split("_")[0][-1], axis=1)
 df["replica"] = df.apply(lambda x: int(x.replica) if x.replica.isnumeric() else None, axis=1)
-df["phase"] = df.apply(lambda x: x.file.split("_")[0].replace("SampleSpectra", "")[4:-1 if x.replica is not None else None].casefold(), axis=1)
+df["phase"] = df.apply(lambda x: x.file.split("_")[0].replace("SampleSpectra", "")[4:].casefold(), axis=1)
+df["phase"] = df.apply(lambda x: x.phase if pd.isnull(x.replica) else x.phase[:-1], axis=1)
 df["objective"] = df.apply(lambda x: int(x.file.split("_")[1].replace("x", "")), axis=1)
 df["substrate"] = df.apply(lambda x: x.file.split("_")[4].casefold(), axis=1)
 df["laser_power[%]"] = df.apply(lambda x: int(x.file.split("_")[5].replace("percent", "")), axis=1)

images/random_forest/vary_smooth_order.svg

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utils/df_utils.py

@@ -0,0 +1,9 @@
+import pandas as pd
+
+def slice_df(df, slice_positions):
+    sliced_dfs = [df[:slice_positions[0]-1].copy(deep=True)]
+    for i in range(len(slice_positions)-1):
+        sliced_dfs.append(df[slice_positions[i]-1:slice_positions[i+1]-1].copy(deep=True))
+    sliced_dfs.append(df[slice_positions[-1]-1:].copy(deep=True))
+    return sliced_dfs
+