Add methods to fit the models and scripts to find optimal weights giv…

…en the fold changes

pyproject.toml

@@ -6,7 +6,12 @@ readme = "README.md"
 requires-python = ">=3.9.0"
 dynamic = ["version"]
 
-dependencies = []
+dependencies = [
+    "pandas",
+    "numpy",
+    "scipy",
+    "seaborn"
+]
 
 license = {text = "BSD-3-Clause"}
 

translocator_models/fit_the_model.py

@@ -0,0 +1,193 @@
+import pandas as pd
+import seaborn as sns
+from fitting_methods import (
+    fit_fold_changes_to_data,
+    get_predicted_fold_changes_matched_dataset,
+    get_predicted_fold_changes_passive_same_luminance,
+    get_predicted_fold_changes_visual_flow,
+)
+from models import arithmetic_sum_model
+
+#  ===============================================
+#  Mean fold changes across datasets
+#  visual flow, 39 clusters
+visual_flow = {}
+visual_flow["RV"] = 2.52
+visual_flow["RVT"] = 2.42
+visual_flow["V"] = 1.88
+visual_flow["VT"] = 2.13
+
+#  passive same luminance, 151 clusters
+passive_same_luminance = {}
+passive_same_luminance["T"] = 1.44
+passive_same_luminance["V"] = 1.16
+passive_same_luminance["VT"] = 1.76
+
+# Re-balanced fold changes, together in the same dataset
+matched_dataset = {}
+matched_dataset["T"] = 1.74
+matched_dataset["V"] = 1.88
+matched_dataset["VT"] = 2.13
+matched_dataset["RV"] = 2.52
+matched_dataset["RVT"] = 2.42
+matched_dataset["RV_slip"] = 2.61
+matched_dataset["RVT_slip"] = 3.045
+
+# Weights of the arithmetic sum model, fitted on the passive same luminance
+# dataset
+b0 = 0.08
+b1 = 0.74
+b2 = 0.53
+
+#  ===============================================
+#  Fitting parameters to visual flow dataset
+print("===============================================")
+print("Visual flow dataset")
+
+best_result = fit_fold_changes_to_data(
+    data=[
+        visual_flow["V"],
+        visual_flow["VT"],
+        visual_flow["RV"],
+        visual_flow["RVT"],
+    ]
+)
+
+k, c, w1, w2, w3 = best_result.x
+
+print(f"fc(v) = {k:.2f} * v + {c:.2f}")
+print(f"v = {w1:.2f} (VF > 0) + {w2:.2f} (T - R > 0)(T - R) + {w3:.2f} R")
+
+predicted_fc = get_predicted_fold_changes_visual_flow(best_result.x)
+
+
+print(f"V:   {predicted_fc[0]:.2f}, mean from data: {visual_flow['V']}")
+print(f"VT:  {predicted_fc[1]:.2f}, mean from data: {visual_flow['VT']}")
+print(f"RV:  {predicted_fc[2]:.2f}, mean from data: {visual_flow['RV']}")
+print(f"RVT: {predicted_fc[3]:.2f}, mean from data: {visual_flow['RVT']}")
+
+
+#  ===============================================
+#  Fitting parameters to passive_same_luminance dataset
+print("===============================================")
+print("Passive same luminance dataset")
+
+
+best_result = fit_fold_changes_to_data(
+    data=[
+        passive_same_luminance["V"],
+        passive_same_luminance["VT"],
+        passive_same_luminance["T"],
+    ],
+    dataset="passive_same_luminance",
+)
+
+
+k, c, w1, w2, w3 = best_result.x
+
+print(f"fc(v) = {k:.2f} * v + {c:.2f}")
+print(f"v = {w1:.2f} (VF > 0) + {w2:.2f} (T - R > 0)(T - R) + {w3:.2f} R")
+
+predicted_fc = get_predicted_fold_changes_passive_same_luminance(best_result.x)
+
+
+print(
+    f"V:  {predicted_fc[0]:.2f}, "
+    + "mean from data: {passive_same_luminance['V']}"
+)
+print(
+    f"VT: {predicted_fc[1]:.2f}, "
+    + "mean from data: {passive_same_luminance['VT']}"
+)
+print(
+    f"T:  {predicted_fc[2]:.2f}, "
+    + "mean from data: {passive_same_luminance['T']}"
+)
+
+
+arithmetic_sum = {}
+arithmetic_sum["VT"] = arithmetic_sum_model(
+    fc_T_VStatic=predicted_fc[2],
+    fc_VF=predicted_fc[0],
+    b0=b0,
+    b1=b1,
+    b2=b2,
+)
+
+print(
+    "VT as predicted with the arithmetic sum + rate model: "
+    + f"{arithmetic_sum['VT']:.2f}"
+)
+
+
+#  ===============================================
+# matched dataset
+print("===============================================")
+print("Visual flow + mismatch matched dataset + T")
+
+
+best_result = fit_fold_changes_to_data(
+    data=[
+        matched_dataset["T"],
+        matched_dataset["V"],
+        matched_dataset["VT"],
+        matched_dataset["RV"],
+        matched_dataset["RVT"],
+        matched_dataset["RV_slip"],
+        matched_dataset["RVT_slip"],
+    ],
+    dataset="matched",
+)
+
+
+k, c, w1, w2, w3 = best_result.x
+
+print(f"fc(v) = {k:.2f} * v + {c:.2f}")
+print(f"v = {w1:.2f} (VF > 0) + {w2:.2f} (T - R > 0)(T - R) + {w3:.2f} R")
+
+predicted_fc = get_predicted_fold_changes_matched_dataset(best_result.x)
+
+print(f"T:   {predicted_fc[0]:.2f}, mean from data: {matched_dataset['T']}")
+print(f"V:   {predicted_fc[1]:.2f}, mean from data: {matched_dataset['V']}")
+print(f"VT:  {predicted_fc[2]:.2f}, mean from data: {matched_dataset['VT']}")
+print(f"RV:  {predicted_fc[3]:.2f}, mean from data: {matched_dataset['RV']}")
+print(f"RVT: {predicted_fc[4]:.2f}, mean from data: {matched_dataset['RVT']}")
+print(
+    f"RV_slip: {predicted_fc[5]:.2f}, "
+    + "mean from data: {matched_dataset['RV_slip']}"
+)
+print(
+    f"RVT_slip: {predicted_fc[6]:.2f}, "
+    + "mean from data: {matched_dataset['RVT_slip']}"
+)
+
+
+arithmetic_sum = {}
+arithmetic_sum["VT"] = arithmetic_sum_model(
+    fc_T_VStatic=predicted_fc[0],
+    fc_VF=predicted_fc[1],
+    b0=b0,
+    b1=b1,
+    b2=b2,
+)
+
+print(
+    "VT as predicted with the arithmetic sum + rate model: "
+    + f"{arithmetic_sum['VT']:.2f}"
+)
+
+df = pd.DataFrame(
+    {
+        "trial_type": matched_dataset.keys(),
+        "data": matched_dataset.values(),
+        "predictions": predicted_fc,
+    },
+)
+df = pd.melt(
+    df,
+    id_vars="trial_type",
+    value_vars=["data", "predictions"],
+    var_name="group",
+    value_name="fold_change",
+)
+sns.barplot(df, x="trial_type", y="fold_change", hue="group")