| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 43, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import cvxpy as cp\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import gymnasium as gym\n", | |
| "import os\n", | |
| "import datetime" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 72, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "class LinearMDPTest:\n", | |
| " def __init__(self, features, num_states, num_actions, alpha=0.05):\n", | |
| " self.features = features\n", | |
| " self.S = num_states\n", | |
| " self.A = num_actions\n", | |
| " self.d = features.shape[1]\n", | |
| " self.alpha = alpha\n", | |
| " self.num_contexts = self.S * self.A \n", | |
| " \n", | |
| " self.counts_sa = np.zeros(self.num_contexts)\n", | |
| " self.counts_sas = np.zeros((self.num_contexts, self.S))\n", | |
| " self.t = 0\n", | |
| " self.stopped = False\n", | |
| " \n", | |
| " # STABILITY FIX: Keep track of the last valid statistic\n", | |
| " self.last_Lt = 0.0\n", | |
| "\n", | |
| " def update_counts(self, s, a, s_next):\n", | |
| " if self.stopped: return\n", | |
| " sa_idx = s * self.A + a\n", | |
| " self.counts_sa[sa_idx] += 1\n", | |
| " self.counts_sas[sa_idx, s_next] += 1\n", | |
| " self.t += 1\n", | |
| "\n", | |
| " def compute_statistic(self):\n", | |
| " # 1. Handle already stopped case\n", | |
| " if self.stopped:\n", | |
| " return {\n", | |
| " \"L_t\": self.final_Lt,\n", | |
| " \"threshold\": self.final_beta,\n", | |
| " \"stopped\": True\n", | |
| " }\n", | |
| "\n", | |
| " # 2. Threshold Calculation (Robust)\n", | |
| " with np.errstate(divide='ignore', invalid='ignore'):\n", | |
| " # Add tiny epsilon to avoid log(0)\n", | |
| " term_inside = np.e * (1 + self.counts_sa / (self.num_contexts - 1))\n", | |
| " log_terms = np.log(term_inside + 1e-12)\n", | |
| " log_terms[np.isinf(log_terms)] = 0\n", | |
| " psi_t = np.sum(log_terms)\n", | |
| " \n", | |
| " beta_t = np.log(1 / self.alpha) + (self.num_contexts - 1) * psi_t\n", | |
| "\n", | |
| " # 3. Optimization Setup\n", | |
| " # We need to find the \"best fitting\" Linear MDP (Null Hypothesis)\n", | |
| " mu = cp.Variable((self.d, self.S))\n", | |
| " P_recon = self.features @ mu\n", | |
| "\n", | |
| " rows, cols = np.nonzero(self.counts_sas)\n", | |
| " vals = self.counts_sas[rows, cols]\n", | |
| "\n", | |
| " # Use vectorized operations for speed and cleaner graph construction\n", | |
| " # P_recon[rows, cols] selects the specific transition probs we observed\n", | |
| " selected_probs = P_recon[rows, cols]\n", | |
| " \n", | |
| " # Maximize sum( count * log(prob) )\n", | |
| " # We add 1e-9 inside log to prevent solver singularities\n", | |
| " objective = cp.Maximize(cp.sum(cp.multiply(vals, cp.log(selected_probs + 1e-6))))\n", | |
| " \n", | |
| " constraints = [\n", | |
| " P_recon >= 0, # Probabilities must be non-negative\n", | |
| " cp.sum(P_recon, axis=1) == 1 # Rows must sum to 1\n", | |
| " ]\n", | |
| "\n", | |
| " prob = cp.Problem(objective, constraints)\n", | |
| "\n", | |
| " # 4. SOLVER STRATEGY (The \"Anti-Crash\" Block)\n", | |
| " max_ll_null = None\n", | |
| " \n", | |
| " # Attempt 1: SCS (Robust for large scale, looser tolerances)\n", | |
| " try:\n", | |
| " prob.solve(solver=cp.SCS, eps=1e-3, max_iters=5000, verbose=False)\n", | |
| " except Exception:\n", | |
| " pass # Just catch it and move to Attempt 2\n", | |
| "\n", | |
| " # Check if Attempt 1 worked\n", | |
| " if prob.status not in [\"optimal\", \"optimal_inaccurate\"]:\n", | |
| " # Attempt 2: ECOS (Better for small/medium conic problems)\n", | |
| " try:\n", | |
| " prob.solve(solver=cp.ECOS, verbose=False)\n", | |
| " except Exception:\n", | |
| " pass \n", | |
| "\n", | |
| " # 5. Result Handling\n", | |
| " # If both solvers failed, or produced garbage/infinity\n", | |
| " if prob.value is None or np.isinf(prob.value) or prob.status in [\"infeasible\", \"unbounded\"]:\n", | |
| " # FALLBACK: Don't return 0. Return the last valid calculation.\n", | |
| " # This keeps the graph line flat instead of crashing down.\n", | |
| " return {\"L_t\": self.last_Lt, \"threshold\": beta_t, \"stopped\": False}\n", | |
| "\n", | |
| " max_ll_null = prob.value\n", | |
| "\n", | |
| " # 6. Empirical Likelihood (The \"Real\" data likelihood)\n", | |
| " with np.errstate(divide='ignore', invalid='ignore'):\n", | |
| " probs_emp = vals / self.counts_sa[rows]\n", | |
| " current_ll = np.sum(vals * np.log(probs_emp + 1e-6))\n", | |
| "\n", | |
| " # 7. Final Statistic Calculation\n", | |
| " L_t = max(0, current_ll - max_ll_null)\n", | |
| " \n", | |
| " # Save this as the new \"last good value\"\n", | |
| " self.last_Lt = L_t\n", | |
| "\n", | |
| " if L_t >= beta_t:\n", | |
| " self.stopped = True\n", | |
| " self.rejection_time = self.t\n", | |
| " self.final_Lt = L_t\n", | |
| " self.final_beta = beta_t\n", | |
| " return {\"L_t\": L_t, \"threshold\": beta_t, \"stopped\": True}\n", | |
| "\n", | |
| " return {\"L_t\": L_t, \"threshold\": beta_t, \"stopped\": False}\n", | |
| "\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 73, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def get_rbf_features(S, A, d):\n", | |
| " # Simple RBF + Bias\n", | |
| " centers = np.linspace(0, S*A, d-1)\n", | |
| " width = (S*A) / (d-1)\n", | |
| " x = np.arange(S*A)\n", | |
| " feats = np.zeros((S*A, d-1))\n", | |
| " for i, c in enumerate(centers):\n", | |
| " feats[:, i] = np.exp(-0.5 * ((x - c)/width)**2)\n", | |
| "\n", | |
| " # Add Bias and Normalize\n", | |
| " feats = np.hstack([np.ones((S*A, 1)), feats])\n", | |
| " feats /= np.sum(feats, axis=1, keepdims=True)\n", | |
| " return feats\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 74, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "class DiscretizedMountainCar:\n", | |
| " def __init__(self, bins=5):\n", | |
| " self.env = gym.make('MountainCar-v0')\n", | |
| " self.bins = bins\n", | |
| " self.S = bins * bins\n", | |
| " self.A = self.env.action_space.n\n", | |
| " # Precompute bins\n", | |
| " self.bins_pos = np.linspace(-1.2, 0.6, bins + 1)\n", | |
| " self.bins_vel = np.linspace(-0.07, 0.07, bins + 1)\n", | |
| "\n", | |
| " def discretize(self, state):\n", | |
| " p, v = state\n", | |
| " pi = np.clip(np.digitize(p, self.bins_pos) - 1, 0, self.bins-1)\n", | |
| " vi = np.clip(np.digitize(v, self.bins_vel) - 1, 0, self.bins-1)\n", | |
| " return pi * self.bins + vi\n", | |
| "\n", | |
| " def step(self, a):\n", | |
| " s, _, term, trunc, _ = self.env.step(a)\n", | |
| " return self.discretize(s), term or trunc" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 87, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Running Simultaneous Test on Dims [3, 5, 7]...\n", | |
| "Environment: Discretized MountainCar (S=64, A=3)\n", | |
| "Step 3200: =3[103.9] d=5[82.0] d=7[67.1] d=3[208.4] d=5[162.9] d=7[132.4] d=3[305.4] d=5[236.4] d=7[189.9] d=3[410.1] d=5[319.2] d=7[257.4] d=3[515.1] d=5[401.7] d=7[324.9] d=3[620.2] d=5[482.7] d=7[389.3] d=3[704.3] d=5[542.9] d=7[433.6] d=3[794.6] d=5[609.0] d=7[483.6] d=3[890.8] d=5[681.7] d=7[540.3] d=3[976.9] d=5[746.1] d=7[589.2] d=3[1076.5] d=5[824.1] d=7[652.7] d=3[1173.7] d=5[898.7] d=7[711.3] d=3[1264.5] d=5[965.8] d=7[763.1] d=3[1358.5] d=5[1038.5] d=7[822.4] d=3[1453.9] d=5[1112.7] d=7[882.1] d=3[1557.9] d=5[1192.1] d=7[945.3] d=3[1654.2] d=5[1266.4] d=7[1005.1] d=3[1832.4] d=5[1408.1] d=7[1130.7] d=3[1920.7] d=5[1487.0] d=7[1193.7] d=3[2015.6] d=5[1559.7] d=7[1251.3] d=3[2100.5] d=5[1622.3] d=7[1299.0] d=3[2199.9] d=5[1700.5] d=7[1362.3] d=3[2293.3] d=5[1768.2] d=7[1414.1] d=3[2379.3] d=5[1832.3] d=7[1460.8] d=3[2483.1] d=5[1912.8] d=7[1525.8] d=3[2610.5] d=5[2007.6] d=7[1606.0] d=3[2710.0] d=5[2084.7] d=7[1668.6] d=3[2807.3] d=5[2160.1] d=7[1728.1] d=3[2910.9] d=5[2245.2] d=7[1794.9] d=3[3005.5] d=5[2317.3] d=7[1851.8] d=3[3098.6] d=5[2388.8] d=7[1908.5] d=3[3202.9] d=5[2467.6] " | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/var/folders/8f/dmfzt2cs10z6wk1vxyg48rkr0000gn/T/ipykernel_57815/1125323771.py:72: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n", | |
| " prob.solve(solver=cp.SCS, eps=1e-3, max_iters=5000, verbose=False)\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Step 99900: d=3[REJECTED] d=5[REJECTED] d=7[REJECTED] 03.1] d=5[2620.6] d=7[2093.9] d=3[3502.5] d=5[2697.3] d=7[2154.1] d=3[3600.2] d=5[2775.4] d=7[2216.9] d=3[3696.1] d=5[2847.9] d=7[2274.2] d=3[3797.4] d=5[2927.4] d=7[2337.8] d=3[3897.0] d=5[3005.9] d=7[2403.1] d=3[3998.7] d=5[3083.1] d=7[2463.9] d=3[4096.0] d=5[3157.9] d=7[2523.9] d=3[4229.9] d=5[3269.2] d=7[2618.3] d=3[4317.3] d=5[3333.2] d=7[2666.3] d=3[4417.6] d=5[3410.9] d=7[2729.1] d=3[4519.3] d=5[3489.8] d=7[2792.8] d=3[4634.3] d=5[3583.2] d=7[2871.9] d=3[4718.9] d=5[3644.8] d=7[2917.2] d=3[4822.6] d=5[3725.0] d=7[2980.4] d=3[4923.9] d=5[3803.7] d=7[3043.8] d=3[5209.4] d=5[4055.7] d=7[3275.1] d=3[5292.0] d=5[4117.7] d=7[3320.1] d=3[5389.2] d=5[4190.4] d=7[3380.7] d=3[5486.3] d=5[4264.0] d=7[3439.6] d=3[5584.5] d=5[4337.2] d=7[3497.0] d=3[5690.3] d=5[4419.1] d=7[3562.5] d=3[5802.3] d=5[4509.6] d=7[3638.1] d=3[5893.6] d=5[4577.5] d=7[3691.3] d=3[5985.7] d=5[4647.6] d=7[3745.5] d=3[6069.2] d=5[4708.2] d=7[3790.1] d=3[6173.6] d=5[4789.9] d=7[3856.9] d=3[6274.9] d=5[4872.9] d=7[3925.5] d=3[6717.6] d=5[5277.7] d=7[4296.3] d=3[6825.5] d=5[5363.2] d=7[4366.0] d=3[6920.2] d=5[5430.3] d=7[4416.2] d=3[7020.9] d=5[5508.0] d=7[4478.6] d=3[7111.0] d=5[5574.4] d=7[4529.7] d=3[7204.5] d=5[5645.1] d=7[4584.9] d=3[7301.5] d=5[5719.3] d=7[4643.2] d=3[7398.8] d=5[5793.1] d=7[4702.4] d=3[7489.7] d=5[5858.1] d=7[4751.8] d=3[7589.3] d=5[5936.4] d=7[4814.4] d=3[7688.5] d=5[6010.9] d=7[4874.2] d=3[7792.7] d=5[6091.5] d=7[4937.9] d=3[7895.5] d=5[6171.8] d=7[5003.1] d=3[7982.7] d=5[6238.3] d=7[5052.7] d=3[8071.1] d=5[6306.9] d=7[5105.9] d=3[8189.2] d=5[6401.9] d=7[5186.3] d=3[8349.3] d=5[6534.6] d=7[5301.7] d=3[8454.9] d=5[6617.8] d=7[5368.8] d=3[8562.0] d=5[6702.6] d=7[5437.9] d=3[8661.0] d=5[6777.0] d=7[5496.6] d=3[8764.0] d=5[6857.2] d=7[5560.7] d=3[8862.8] d=5[6932.5] d=7[5620.8] d=3[8957.5] d=5[7005.0] d=7[5678.5] d=3[9054.5] d=5[7079.1] d=7[5737.6] d=3[9158.2] d=5[7161.1] d=7[5804.3] d=3[9262.6] d=5[7242.8] d=7[5870.9] d=3[9363.5] d=5[7322.0] d=7[5934.8] d=3[9440.4] d=5[7377.6] d=7[5974.4] d=3[9509.4] d=5[7422.2] d=7[6005.0] d=3[9615.3] d=5[7506.3] d=7[6074.1] d=3[9709.1] d=5[7576.4] d=7[6127.2] d=3[9792.5] d=5[7634.9] d=7[6170.9] d=3[9886.7] d=5[7708.6] d=7[6229.6] d=3[9969.3] d=5[7765.7] d=7[6270.9] d=3[10115.2] d=5[7887.3] d=7[6373.0] d=3[10222.4] d=5[7972.1] d=7[6442.3] d=3[10484.0] d=5[8205.9] d=7[6655.3] d=3[10580.8] d=5[8280.1] d=7[6714.1] d=3[10670.6] d=5[8346.3] d=7[6765.0] d=3[10769.5] d=5[8422.6] d=7[6825.8] d=3[10867.8] d=5[8498.3] d=7[6886.9] d=3[10968.5] d=5[8576.0] d=7[6948.3] d=3[11072.4] d=5[8658.1] d=7[7014.9] d=3[11176.7] d=5[8739.8] d=7[7081.1] d=3[11284.3] d=5[8824.8] d=7[7149.8] d=3[11366.3] d=5[8884.0] d=7[7193.6] d=3[11463.0] d=5[8957.5] d=7[7252.0] d=3[11545.8] d=5[9017.2] d=7[7296.2] d=3[11644.7] d=5[9094.4] d=7[7358.4] d=3[11742.2] d=5[9168.4] d=7[7417.4] d=3[11845.2] d=5[9249.4] d=7[7483.2] d=3[11924.2] d=5[9303.2] d=7[7521.7] d=3[12029.6] d=5[9385.8] d=7[7589.1] d=3[12133.7] d=5[9466.7] d=7[7654.0] d=3[12236.4] d=5[9548.0] d=7[7719.2] d=3[12340.1] d=5[9629.0] d=7[7784.6] d=3[12441.5] d=5[9708.5] d=7[7848.8] d=3[12546.6] d=5[9791.0] d=7[7916.0] d=3[12649.7] d=5[9871.9] d=7[7981.7] d=3[12751.3] d=5[9950.9] d=7[8046.0] d=3[12852.7] d=5[10028.7] d=7[8107.8] d=3[12952.6] d=5[10107.1] d=7[8170.9] d=3[13055.9] d=5[10186.9] d=7[8235.2] d=3[13162.7] d=5[10270.6] d=7[8304.2] d=3[13505.8] d=5[10575.1] d=7[8576.7] d=3[13612.4] d=5[10658.9] d=7[8645.3] d=3[13732.0] d=5[10754.5] d=7[8726.0] d=3[13837.8] d=5[10838.3] d=7[8794.0] d=3[14131.1] d=5[11097.0] d=7[9026.6] d=3[14221.0] d=5[11165.0] d=7[9078.5] d=3[14311.4] d=5[11233.1] d=7[9133.9] d=3[14415.7] d=5[11315.3] d=7[9200.1] d=3[14513.0] d=5[11387.8] d=7[9257.5] d=3[14615.4] d=5[11468.1] d=7[9321.5] d=3[14718.8] d=5[11548.8] d=7[9386.6] d=3[14817.5] d=5[11625.8] d=7[9447.7] d=3[14914.6] d=5[11699.2] d=7[9505.3] d=3[15019.0] d=5[11780.4] d=7[9572.5] d=3[15143.4] d=5[11881.6] d=7[9657.3] d=3[15221.4] d=5[11938.0] d=7[9697.7] d=3[15323.6] d=5[12018.5] d=7[9763.3] d=3[15406.9] d=5[12077.7] d=7[9808.6] d=3[15503.8] d=5[12152.0] d=7[9867.6] d=3[15595.1] d=5[12219.9] d=7[9917.7] d=3[15697.6] d=5[12299.8] d=7[9982.7] d=3[15804.4] d=5[12383.9] d=7[10052.1] d=3[15986.1] d=5[12539.7] d=7[10188.9] d=3[16094.0] d=5[12623.8] d=7[10256.3] d=3[16197.1] d=5[12702.7] d=7[10322.1] d=3[16302.2] d=5[12785.8] d=7[10389.6] d=3[16423.1] d=5[12881.5] d=7[10469.6] d=3[16527.7] d=5[12964.2] d=7[10536.9] d=3[16626.6] d=5[13040.1] d=7[10596.4] d=3[16728.7] d=5[13119.5] d=7[10660.4] d=3[16835.3] d=5[13203.0] d=7[10726.9] d=3[16938.2] d=5[13282.0] d=7[10791.9] d=3[17165.0] d=5[13480.9] d=7[10969.5] d=3[17244.2] d=5[13537.5] d=7[11010.3] d=3[17344.1] d=5[13614.0] d=7[11071.6] d=3[17446.1] d=5[13693.8] d=7[11136.1] d=3[17543.0] d=5[13767.4] d=7[11194.3] d=3[17635.7] d=5[13837.3] d=7[11248.0] d=3[17726.7] d=5[13905.5] d=7[11301.0] d=3[17832.1] d=5[13987.8] d=7[11367.2] d=3[17933.9] d=5[14067.7] d=7[11432.6] d=3[18027.3] d=5[14139.1] d=7[11489.8] d=3[18126.0] d=5[14215.3] d=7[11549.9] d=3[18221.6] d=5[14288.2] d=7[11607.8] d=3[18316.3] d=5[14360.8] d=7[11664.9] d=3[18422.4] d=5[14443.8] d=7[11732.8] d=3[18516.7] d=5[14516.1] d=7[11790.5] d=3[18614.3] d=5[14591.9] d=7[11850.3] d=3[18720.5] d=5[14674.8] d=7[11918.4] d=3[18824.3] d=5[14756.7] d=7[11984.0] d=3[19084.6] d=5[14983.4] d=7[12188.4] d=3[19191.4] d=5[15067.5] d=7[12256.4] d=3[19292.0] d=5[15147.1] d=7[12319.4] d=3[19392.1] d=5[15223.6] d=7[12381.6] d=3[19497.7] d=5[15305.6] d=7[12447.5] d=3[19595.3] d=5[15379.8] d=7[12506.6] d=3[19737.6] d=5[15500.2] d=7[12610.1] d=3[19829.3] d=5[15568.6] d=7[12666.0] d=3[19932.1] d=5[15648.4] d=7[12729.3] d=3[20032.3] d=5[15726.2] d=7[12791.7] d=3[20136.6] d=5[15809.2] d=7[12859.3] d=3[20240.7] d=5[15889.9] d=7[12924.9] d=3[20343.1] d=5[15968.7] d=7[12989.3] d=3[20448.9] d=5[16053.2] d=7[13058.1] d=3[20554.4] d=5[16136.0] d=7[13125.3] d=3[20661.0] d=5[16220.6] d=7[13194.8] d=3[20934.5] d=5[16457.7] d=7[13402.4] d=3[21044.8] d=5[16545.8] d=7[13474.3] d=3[21142.3] d=5[16619.7] d=7[13532.9] d=3[21240.3] d=5[16695.0] d=7[13593.0] d=3[21340.4] d=5[16772.9] d=7[13656.9] d=3[21444.8] d=5[16854.6] d=7[13723.2] d=3[21552.4] d=5[16939.2] d=7[13791.8] d=3[21650.3] d=5[17015.6] d=7[13853.6] d=3[21742.2] d=5[17084.9] d=7[13906.4] d=3[21891.4] d=5[17207.4] d=7[14009.7] d=3[22008.0] d=5[17301.3] d=7[14089.4] d=3[22112.1] d=5[17381.8] d=7[14155.2] d=3[22211.1] d=5[17459.0] d=7[14217.8] d=3[22299.7] d=5[17524.8] d=7[14268.7] d=3[22405.3] d=5[17607.2] d=7[14336.2] d=3[22485.4] d=5[17670.1] d=7[14382.7] d=3[22571.4] d=5[17731.6] d=7[14428.0] d=3[22661.7] d=5[17799.6] d=7[14480.1] d=3[22759.3] d=5[17873.4] d=7[14539.4] d=3[22937.2] d=5[18026.0] d=7[14675.1] d=3[23449.5] d=5[18488.5] d=7[15090.4] d=3[23553.1] d=5[18568.4] d=7[15154.6] d=3[23656.1] d=5[18648.4] d=7[15219.1] d=3[23753.9] d=5[18724.6] d=7[15278.7] d=3[23861.5] d=5[18808.4] d=7[15347.3] d=3[23940.8] d=5[18865.8] d=7[15391.1] d=3[24043.7] d=5[18944.6] d=7[15455.2] d=3[24148.5] d=5[19025.8] d=7[15520.1] d=3[24424.6] d=5[19269.2] d=7[15738.0] d=3[24528.1] d=5[19350.3] d=7[15803.8] d=3[24629.8] d=5[19428.5] d=7[15865.9] d=3[24723.7] d=5[19498.2] d=7[15920.1] d=3[24824.5] d=5[19577.2] d=7[15983.8] d=3[24908.5] d=5[19638.3] d=7[16028.6] d=3[25013.0] d=5[19721.2] d=7[16097.1] d=3[25123.0] d=5[19807.2] d=7[16167.1] d=3[25225.1] d=5[19886.5] d=7[16229.7] d=3[25326.9] d=5[19966.0] d=7[16294.0] d=3[25412.0] d=5[20029.8] d=7[16342.4] d=3[25515.1] d=5[20110.5] d=7[16407.6] d=3[25658.3] d=5[20230.4] d=7[16512.2] d=3[25759.2] d=5[20308.1] d=7[16574.7] d=3[25865.6] d=5[20392.8] d=7[16644.0] d=3[25973.1] d=5[20477.8] d=7[16713.9] d=3[26076.5] d=5[20559.2] d=7[16779.3] d=3[26163.0] d=5[20622.5] d=7[16827.9] d=3[26244.0] d=5[20680.0] d=7[16871.1] d=3[26351.7] d=5[20765.6] d=7[16940.2] d=3[26455.1] d=5[20846.5] d=7[17006.5] d=3[26563.0] d=5[20930.5] d=7[17075.8] d=3[26662.8] d=5[21008.4] d=7[17138.3] d=3[26767.9] d=5[21091.1] d=7[17205.6] d=3[26872.1] d=5[21171.9] d=7[17270.7] d=3[26977.0] d=5[21253.4] d=7[17337.1] d=3[27075.5] d=5[21330.0] d=7[17397.8] d=3[27166.5] d=5[21399.9] d=7[17452.5] d=3[27271.3] d=5[21481.0] d=7[17518.1] d=3[27350.9] d=5[21535.8] d=7[17557.7] d=3[27453.8] d=5[21616.5] d=7[17621.9] d=3[27553.4] d=5[21693.0] d=7[17683.2] d=3[27659.0] d=5[21776.0] d=7[17750.3] d=3[27843.9] d=5[21933.1] d=7[17888.4] d=3[28078.3] d=5[22139.0] d=7[18071.1] d=3[28179.6] d=5[22217.7] d=7[18133.7] d=3[28282.6] d=5[22298.6] d=7[18199.6] d=3[28386.0] d=5[22379.3] d=7[18265.4] d=3[28490.9] d=5[22460.8] d=7[18330.6] d=3[28598.4] d=5[22545.3] d=7[18400.0] d=3[28706.8] d=5[22631.2] d=7[18470.7] d=3[28793.5] d=5[22698.7] d=7[18523.3] d=3[28877.7] d=5[22759.0] d=7[18568.6] d=3[28985.1] d=5[22844.5] d=7[18638.1] d=3[29074.8] d=5[22913.7] d=7[18692.9] d=3[29175.1] d=5[22991.6] d=7[18756.0] d=3[29281.0] d=5[23074.1] d=7[18822.7] d=3[29550.4] d=5[23311.9] d=7[19037.7] d=3[29970.8] d=5[23686.5] d=7[19369.7] d=3[30073.2] d=5[23765.3] d=7[19432.8] d=3[30176.7] d=5[23844.6] d=7[19497.1] d=3[30277.2] d=5[23923.8] d=7[19560.8] d=3[30382.2] d=5[24006.2] d=7[19628.1] d=3[30485.1] d=5[24086.5] d=7[19692.9] d=3[30590.6] d=5[24168.9] d=7[19759.2] d=3[30694.3] d=5[24250.4] d=7[19825.1] d=3[30842.5] d=5[24372.6] d=7[19927.7] d=3[30948.1] d=5[24450.7] d=7[19989.9] d=3[31053.1] d=5[24531.3] d=7[20053.7] d=3[31160.6] d=5[24614.5] d=7[20120.5] d=3[31255.8] d=5[24685.6] d=7[20175.0] d=3[31357.6] d=5[24767.0] d=7[20240.9] d=3[31596.2] d=5[24973.5] d=7[20422.7] d=3[31699.5] d=5[25052.4] d=7[20486.2] d=3[31802.0] d=5[25131.5] d=7[20550.3] d=3[31905.6] d=5[25210.8] d=7[20613.3] d=3[32009.6] d=5[25292.6] d=7[20679.8] d=3[32255.4] d=5[25509.3] d=7[20877.0] d=3[32675.2] d=5[25880.5] d=7[21206.5] d=3[32780.3] d=5[25963.1] d=7[21274.3] d=3[32881.3] d=5[26041.0] d=7[21336.2] d=3[32971.6] d=5[26109.7] d=7[21389.7] d=3[33074.6] d=5[26189.1] d=7[21453.2] d=3[33177.6] d=5[26269.9] d=7[21518.0] d=3[33290.3] d=5[26359.6] d=7[21595.2] d=3[33387.2] d=5[26432.7] d=7[21652.2] d=3[33493.4] d=5[26516.5] d=7[21716.9] d=3[33578.6] d=5[26578.1] d=7[21761.0] d=3[33681.8] d=5[26658.7] d=7[21825.4] d=3[33774.1] d=5[26726.5] d=7[21880.0] d=3[33881.8] d=5[26808.9] d=7[21946.0] d=3[33980.8] d=5[26887.3] d=7[22013.1] d=3[34076.0] d=5[26960.6] d=7[22070.0] d=3[34163.5] d=5[27023.5] d=7[22111.9] d=3[34268.2] d=5[27105.9] d=7[22182.9] d=3[34376.1] d=5[27191.0] d=7[22253.4] d=3[34480.5] d=5[27272.4] d=7[22316.0] d=3[34581.8] d=5[27351.5] d=7[22379.4] d=3[34687.0] d=5[27433.7] d=7[22445.6] d=3[34790.5] d=5[27513.9] d=7[22511.2] d=3[34867.7] d=5[27569.7] d=7[22550.7] d=3[34976.3] d=5[27656.6] d=7[22620.9] d=3[35075.9] d=5[27731.8] d=7[22686.6] d=3[35172.7] d=5[27808.0] d=7[22746.2] d=3[35282.8] d=5[27893.2] d=7[22815.9] d=3[35374.3] d=5[27962.0] d=7[22868.9] d=3[35475.2] d=5[28038.8] d=7[22931.0] d=3[35576.0] d=5[28116.9] d=7[22993.9] d=3[35679.4] d=5[28199.2] d=7[23059.7] d=3[35786.0] d=5[28283.2] d=7[23127.7] d=3[36041.7] d=5[28512.6] d=7[23335.7] d=3[36145.1] d=5[28591.3] d=7[23397.2] d=3[36239.5] d=5[28662.3] d=7[23451.7] d=3[36342.2] d=5[28741.6] d=7[23520.5] d=3[36449.6] d=5[28827.2] d=7[23589.8] d=3[36556.4] d=5[28910.2] d=7[23659.4] d=3[36869.0] d=5[29192.7] d=7[23914.4] d=3[36978.0] d=5[29275.7] d=7[23982.0] d=3[37275.1] d=5[29536.2] d=7[24214.1] d=3[37381.6] d=5[29620.6] d=7[24281.5] d=3[37486.1] d=5[29706.0] d=7[24351.9] d=3[37583.7] d=5[29779.2] d=7[24407.8] d=3[37689.8] d=5[29860.0] d=7[24472.6] d=3[37794.5] d=5[29943.8] d=7[24541.2] d=3[37919.1] d=5[30043.7] d=7[24622.7] d=3[38030.6] d=5[30127.2] d=7[24692.1] d=3[38137.9] d=5[30216.4] d=7[24765.6] d=3[38234.6] d=5[30290.5] d=7[24823.5] d=3[38336.2] d=5[30372.5] d=7[24889.0] d=3[38450.0] d=5[30455.4] d=7[24957.9] d=3[38551.8] d=5[30540.0] d=7[25027.1] d=3[38651.0] d=5[30614.1] d=7[25086.3] d=3[38754.8] d=5[30695.9] d=7[25151.4] d=3[38850.7] d=5[30767.7] d=7[25207.5] d=3[38952.9] d=5[30849.1] d=7[25273.7] d=3[39041.5] d=5[30914.1] d=7[25325.1] d=3[39140.8] d=5[30990.6] d=7[25386.1] d=3[39240.6] d=5[31067.2] d=7[25447.8] d=3[39346.2] d=5[31152.1] d=7[25518.6] d=3[39457.4] d=5[31238.1] d=7[25589.8] d=3[39563.9] d=5[31321.1] d=7[25652.8] d=3[39653.3] d=5[31388.1] d=7[25705.2] d=3[39757.2] d=5[31469.8] d=7[25774.1] d=3[39858.8] d=5[31549.3] d=7[25838.8] d=3[39964.8] d=5[31631.8] d=7[25904.8] d=3[40067.8] d=5[31710.1] d=7[25969.8] d=3[40170.0] d=5[31793.4] d=7[26036.8] d=3[40273.7] d=5[31875.7] d=7[26104.8] d=3[40380.2] d=5[31957.7] d=7[26168.0] d=3[40488.0] d=5[32039.3] d=7[26237.9] d=3[40589.4] d=5[32119.2] d=7[26301.5] d=3[40674.3] d=5[32182.5] d=7[26349.4] d=3[40774.7] d=5[32258.9] d=7[26410.8] d=3[40879.4] d=5[32342.8] d=7[26479.4] d=3[40981.0] d=5[32421.3] d=7[26542.9] d=3[41072.9] d=5[32490.1] d=7[26595.8] d=3[41181.2] d=5[32575.0] d=7[26667.4] d=3[41288.9] d=5[32659.6] d=7[26735.1] d=3[41417.6] d=5[32762.1] d=7[26821.3] d=3[41514.0] d=5[32836.1] d=7[26880.3] d=3[41619.5] d=5[32917.1] d=7[26945.3] d=3[41727.6] d=5[33004.3] d=7[27015.6] d=3[41919.5] d=5[33168.5] d=7[27159.4] d=3[41997.3] d=5[33226.4] d=7[27197.8] d=3[42099.2] d=5[33305.5] d=7[27261.2] d=3[42210.0] d=5[33391.7] d=7[27332.3] d=3[42404.6] d=5[33563.0] d=7[27486.2] d=3[42516.0] d=5[33646.1] d=7[27554.5] d=3[42629.7] d=5[33738.5] d=7[27630.5] d=3[42729.1] d=5[33815.2] d=7[27690.4] d=3[42835.2] d=5[33895.6] d=7[27757.6] d=3[42943.6] d=5[33980.7] d=7[27827.1] d=3[43051.2] d=5[34067.2] d=7[27897.5] d=3[43150.1] d=5[34144.7] d=7[27959.2] d=3[43245.3] d=5[34218.9] d=7[28016.8] d=3[43337.8] d=5[34287.2] d=7[28070.9] d=3[43444.8] d=5[34369.4] d=7[28139.6] d=3[43540.4] d=5[34442.4] d=7[28195.0] d=3[43645.7] d=5[34525.6] d=7[28263.5] d=3[43750.0] d=5[34605.9] d=7[28328.2] d=3[43852.6] d=5[34687.5] d=7[28394.5] d=3[43949.6] d=5[34761.2] d=7[28452.9] d=3[44043.0] d=5[34833.3] d=7[28505.9] d=3[44149.2] d=5[34915.0] d=7[28574.7] d=3[44242.7] d=5[34986.7] d=7[28631.7] d=3[44339.3] d=5[35058.7] d=7[28686.5] d=3[44443.7] d=5[35142.0] d=7[28753.3] d=3[REJECTED] d=5[35223.8] d=7[28819.6] d=5[35304.4] d=7[28885.6] d=5[35387.5] d=7[28953.9] d=5[35463.4] d=7[29015.9] d=5[35547.7] d=7[29084.9] d=5[35629.1] d=7[29150.8] d=5[35710.8] d=7[29214.8] d=5[35792.5] d=7[29281.3] d=5[35876.5] d=7[29350.2] d=5[35955.1] d=7[29412.5] d=5[36036.3] d=7[29478.9] d=5[36155.2] d=7[29582.8] d=5[36234.6] d=7[29645.5] d=5[36350.1] d=7[29745.1] d=5[36429.3] d=7[29810.1] d=5[36508.7] d=7[29874.8] d=5[36582.2] d=7[29933.4] d=5[36664.0] d=7[29999.6] d=5[36747.8] d=7[30067.1] d=5[36828.8] d=7[30131.9] d=5[36911.2] d=7[30198.9] d=5[36993.2] d=7[30265.4] d=5[37074.7] d=7[30332.7] d=5[37158.7] d=7[30400.8] d=5[37244.5] d=7[30469.8] d=5[37442.8] d=7[30649.2] d=5[37516.8] d=7[30708.1] d=5[37598.3] d=7[30774.2] d=5[37664.7] d=7[30824.6] d=5[37734.9] d=7[30880.8] d=5[37809.2] d=7[30939.2] d=5[37889.6] d=7[31004.6] d=5[37956.2] d=7[31054.5] d=5[38040.3] d=7[31123.0] d=5[38109.7] d=7[31176.4] d=5[38194.5] d=7[31245.2] d=5[38271.6] d=7[31307.9] d=5[38346.5] d=7[31367.3] d=5[38411.0] d=7[31416.2] d=5[38491.0] d=7[31481.5] d=5[38578.5] d=7[31552.3] d=5[38914.4] d=7[31862.2] d=5[39000.5] d=7[31933.1] d=5[39083.6] d=7[32001.1] d=5[39169.6] d=7[32070.9] d=5[39263.3] d=7[32148.8] d=5[39328.4] d=7[32197.9] d=5[39389.6] d=7[32240.4] d=5[39465.6] d=7[32302.2] d=5[39548.9] d=7[32369.4] d=5[39631.6] d=7[32437.3] d=5[39706.9] d=7[32496.6] d=5[39786.4] d=7[32559.7] d=5[39866.9] d=7[32625.0] d=5[39950.4] d=7[32692.3] d=5[40044.6] d=7[32774.0] d=5[40130.4] d=7[32842.4] d=5[40207.1] d=7[32905.5] d=5[40280.8] d=7[32962.3] d=5[40360.2] d=7[33027.7] d=5[40429.6] d=7[33080.1] d=5[40511.3] d=7[33147.1] d=5[40593.7] d=7[33215.3] d=5[40678.8] d=7[33285.3] d=5[40759.0] d=7[33350.9] d=5[40843.0] d=7[33417.3] d=5[40914.2] d=7[33473.5] d=5[40992.2] d=7[33534.0] d=5[41076.8] d=7[33603.0] d=5[41156.1] d=7[33667.0] d=5[41277.8] d=7[33768.0] d=5[41386.6] d=7[33863.1] d=5[41437.9] d=7[33904.3] d=5[41527.0] d=7[33970.8] d=5[41593.0] d=7[34022.7] d=5[41667.8] d=7[34080.1] d=5[41732.9] d=7[34128.8] d=5[41795.8] d=7[34175.1] d=5[41858.9] d=7[34244.3] d=5[41949.8] d=7[34314.3] d=5[42023.5] d=7[34375.2] d=5[42104.3] d=7[34441.3] d=5[42184.1] d=7[34505.9] d=5[42343.1] d=7[34643.7] d=5[42415.0] d=7[34701.5] d=5[42522.9] d=7[34772.6] d=5[42605.3] d=7[34840.5] d=5[42684.5] d=7[34904.9] d=5[42756.7] d=7[34960.8] d=5[42811.6] d=7[35000.5] d=5[42895.6] d=7[35068.2] d=5[42975.9] d=7[35132.5] d=5[43043.9] d=7[35185.9] d=5[43127.6] d=7[35253.3] d=5[43206.6] d=7[35318.6] d=5[43288.7] d=7[35385.1] d=5[43350.5] d=7[35431.5] d=5[43427.6] d=7[35493.4] d=5[43489.0] d=7[35561.3] d=5[43579.7] d=7[35627.0] d=5[43645.6] d=7[35670.7] d=5[43724.6] d=7[35731.1] d=5[43804.7] d=7[35796.8] d=5[43883.9] d=7[35858.2] d=5[43940.8] d=7[35899.9] d=5[43998.4] d=7[35942.1] d=5[44078.4] d=7[36006.9] d=5[44141.6] d=7[36054.3] d=5[44224.5] d=7[36122.0] d=5[44306.6] d=7[36192.0] d=5[44386.6] d=7[36254.0] d=5[44467.7] d=7[36319.3] d=5[44517.5] d=7[36355.0] d=5[44601.8] d=7[36422.7] d=5[44688.6] d=7[36493.3] d=5[44902.3] d=7[36690.5] d=5[44961.9] d=7[36735.1] d=5[45031.4] d=7[36789.2] d=5[45113.7] d=7[36852.4] d=5[45194.1] d=7[36915.8] d=5[45272.2] d=7[36979.3] d=5[REJECTED] d=7[37071.9] d=7[37139.0] d=7[37285.1] d=7[37345.8] d=7[37411.4] d=7[37478.0] d=7[37502.3] d=7[37571.6] d=7[37654.5] d=7[37721.1] d=7[37787.6] d=7[37859.3] d=7[37925.1] d=7[37991.7] d=7[38059.0] d=7[38120.0] d=7[38181.8] d=7[38216.9] d=7[38276.5] d=7[38337.5] d=7[38397.2] d=7[38466.2] d=7[38532.2] d=7[38594.3] d=7[38663.3] d=7[38726.2] d=7[38784.2] d=7[38848.3] d=7[38915.1] d=7[38978.0] d=7[39021.7] d=7[39079.7] d=7[39139.8] d=7[39208.2] d=7[39271.3] d=7[39341.3] d=7[39409.2] d=7[39449.4] d=7[39498.3] d=7[39562.1] d=7[39624.4] d=7[39688.2] d=7[39753.2] d=7[39819.7] d=7[39886.3] d=7[39953.1] d=7[40015.1] d=7[40084.9] d=7[40147.3] d=7[40199.9] d=7[40284.4] d=7[40345.3] d=7[40412.7] d=7[40473.7] d=7[40536.7] d=7[40597.3] d=7[40662.1] d=7[40722.8] d=7[40777.0] d=7[40845.6] d=7[40953.4] d=7[41018.2] d=7[41080.3] d=7[41142.2] d=7[41207.2] d=7[41275.9] d=7[41386.2] d=7[41451.1] d=7[41505.3] d=7[41567.9] d=7[41636.3] d=7[41704.3] d=7[41772.5] d=7[41828.1] d=7[41889.4] d=7[41959.2] d=7[42024.5] d=7[42090.2] d=7[42149.7] d=7[42212.6] d=7[42273.6] d=7[42338.8] d=7[42381.8] d=7[42434.2] d=7[42474.4] d=7[42535.4] d=7[42591.3] d=7[42658.3] d=7[42723.4] d=7[42780.1] d=7[42835.7] d=7[42905.5] d=7[43087.6] d=7[43156.9] d=7[43226.2] d=7[43293.1] d=7[43362.3] d=7[43418.4] d=7[43483.5] d=7[43527.2] d=7[43578.1] d=7[43640.2] d=7[43707.1] d=7[43767.5] d=7[43833.4] d=7[43900.2] d=7[43965.1] d=7[44029.4] d=7[44070.3] d=7[44137.4] d=7[44202.9] d=7[44267.0] d=7[44329.8] d=7[44401.1] d=7[44607.5] d=7[44665.5] d=7[44730.8] d=7[44790.2] d=7[44855.5] d=7[44923.1] d=7[44986.7] d=7[45059.2] d=7[45118.1] d=7[45214.3] d=7[45528.8] d=7[45591.8] d=7[45656.7] d=7[45727.0] d=7[45781.3] d=7[45845.2] d=7[45897.3] d=7[45964.4] d=7[REJECTED] " | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "BINS = 8\n", | |
| "CHECK_INTERVAL = 100\n", | |
| "MAX_STEPS = 100000\n", | |
| "\n", | |
| "# We will test these 3 dimensions simultaneously\n", | |
| "# d=3: Very stiff model (should reject fast)\n", | |
| "# d=6: Moderate model\n", | |
| "# d=12: Flexible model (might overfit and reject slowly)\n", | |
| "TEST_DIMS = [3, 5, 7]\n", | |
| "\n", | |
| "env = DiscretizedMountainCar(bins=BINS)\n", | |
| "\n", | |
| "# Initialize Testers\n", | |
| "testers = {}\n", | |
| "histories = {d: {'L': [], 'beta': [], 'steps': []} for d in TEST_DIMS}\n", | |
| "\n", | |
| "for d in TEST_DIMS:\n", | |
| " feats = get_rbf_features(env.S, env.A, d)\n", | |
| " testers[d] = LinearMDPTest(feats, env.S, env.A, alpha=0.01)\n", | |
| "\n", | |
| "print(f\"Running Simultaneous Test on Dims {TEST_DIMS}...\")\n", | |
| "print(f\"Environment: Discretized MountainCar (S={env.S}, A={env.A})\")\n", | |
| "\n", | |
| "curr_s = env.discretize(env.env.reset()[0])\n", | |
| "\n", | |
| "for t in range(MAX_STEPS):\n", | |
| " action = np.random.choice(env.A)\n", | |
| " next_s, done = env.step(action)\n", | |
| "\n", | |
| " # 1. Update ALL testers with the EXACT SAME transition\n", | |
| " for d in TEST_DIMS:\n", | |
| " testers[d].update_counts(curr_s, action, next_s)\n", | |
| "\n", | |
| " # 2. Check Statistics Periodically\n", | |
| " if t % CHECK_INTERVAL == 0 and t > 50:\n", | |
| " print(f\"\\rStep {t}: \", end=\"\")\n", | |
| "\n", | |
| " for d in TEST_DIMS:\n", | |
| " # If already rejected, just append the final frozen value\n", | |
| " # (handled inside compute_statistic if stopped is True)\n", | |
| " res = testers[d].compute_statistic()\n", | |
| "\n", | |
| " histories[d]['L'].append(res['L_t'])\n", | |
| " histories[d]['beta'].append(res['threshold'])\n", | |
| " histories[d]['steps'].append(t)\n", | |
| "\n", | |
| " status = \"REJECTED\" if res['stopped'] else f\"{res['L_t']:.1f}\"\n", | |
| " print(f\"d={d}[{status}] \", end=\"\")\n", | |
| "\n", | |
| " if done: curr_s = env.discretize(env.env.reset()[0])\n", | |
| " else: curr_s = next_s" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 89, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "\n", | |
| "Saving data to: linear_mdp_data/experiment_2026-02-17_15-32-33.npz ...\n", | |
| "Save complete.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "timestamp = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n", | |
| "folder_name = \"linear_mdp_data\"\n", | |
| "os.makedirs(folder_name, exist_ok=True)\n", | |
| "filename = os.path.join(folder_name, f\"experiment_{timestamp}.npz\")\n", | |
| "\n", | |
| "# Collect final rejection states for easier analysis later\n", | |
| "final_stats = {}\n", | |
| "for d in TEST_DIMS:\n", | |
| " final_stats[d] = {\n", | |
| " 'stopped': testers[d].stopped,\n", | |
| " 'rejection_time': getattr(testers[d], 'rejection_time', None),\n", | |
| " 'final_Lt': getattr(testers[d], 'final_Lt', None)\n", | |
| " }\n", | |
| "\n", | |
| "print(f\"\\n\\nSaving data to: {filename} ...\")\n", | |
| "np.savez(\n", | |
| " filename, \n", | |
| " histories=histories, # The main time-series data\n", | |
| " test_dims=TEST_DIMS, # Configuration\n", | |
| " final_stats=final_stats, # Summary of results\n", | |
| " settings={'bins': BINS, 'max_steps': MAX_STEPS, 'interval': CHECK_INTERVAL}\n", | |
| ")\n", | |
| "print(\"Save complete.\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 88, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/var/folders/8f/dmfzt2cs10z6wk1vxyg48rkr0000gn/T/ipykernel_57815/3748145875.py:3: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", | |
| " colormap = plt.cm.get_cmap('viridis', len(TEST_DIMS))\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 1200x700 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(12, 7))\n", | |
| "\n", | |
| "colormap = plt.cm.get_cmap('viridis', len(TEST_DIMS))\n", | |
| "colors = {d: colormap(i) for i, d in enumerate(TEST_DIMS)}\n", | |
| "\n", | |
| "ref_d = TEST_DIMS[0]\n", | |
| "if len(histories[ref_d]['steps']) > 0:\n", | |
| " plt.plot(histories[ref_d]['steps'], histories[ref_d]['beta'],\n", | |
| " linestyle='--', color='black', label=r'Threshold ($\\beta_t$)', linewidth=2)\n", | |
| "\n", | |
| "for d in TEST_DIMS:\n", | |
| " if not histories[d]['steps']: continue\n", | |
| "\n", | |
| " plt.plot(histories[d]['steps'], histories[d]['L'],\n", | |
| " label=f'Statistic (d={d})', color=colors[d], linewidth=2)\n", | |
| "\n", | |
| " tester = testers[d]\n", | |
| " if tester.stopped:\n", | |
| " rej_t = tester.rejection_time\n", | |
| " final_val = getattr(tester, 'final_Lt', histories[d]['L'][-1])\n", | |
| "\n", | |
| " plt.scatter([rej_t], [final_val], color=colors[d], s=100, zorder=5, edgecolors='white')\n", | |
| " plt.text(rej_t, final_val + 5, f'Reject d={d}', color=colors[d], fontweight='bold')\n", | |
| "\n", | |
| "plt.title(f\"Impact of Rank ($d$) on Linearity Rejection Speed\\n(Lower Rank = Faster Rejection)\")\n", | |
| "plt.xlabel(\"Time Steps\")\n", | |
| "plt.ylabel(\"Statistic ($L_t$)\")\n", | |
| "plt.legend()\n", | |
| "plt.grid(True, alpha=0.3)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Aggregated Data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 90, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Found 20 runs. Aggregating...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/var/folders/8f/dmfzt2cs10z6wk1vxyg48rkr0000gn/T/ipykernel_57815/1032728890.py:91: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", | |
| " colormap = plt.cm.get_cmap('viridis', len(test_dims))\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 1200x700 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import warnings # <--- Add this\n", | |
| "import os\n", | |
| "import glob\n", | |
| "\n", | |
| "def load_and_plot_aggregated_runs(folder_name=\"linear_mdp_data\"):\n", | |
| " # 1. Find all files\n", | |
| " files = glob.glob(os.path.join(folder_name, \"experiment_*.npz\"))\n", | |
| " if not files:\n", | |
| " print(f\"No data found in {folder_name}\")\n", | |
| " return\n", | |
| "\n", | |
| " print(f\"Found {len(files)} runs. Aggregating...\")\n", | |
| "\n", | |
| " # 2. Storage for aggregation\n", | |
| " # Structure: raw_data[d]['L'] = list of arrays (one per run)\n", | |
| " raw_data = {} \n", | |
| " test_dims = None\n", | |
| "\n", | |
| " # 3. Load Data\n", | |
| " for f in files:\n", | |
| " with np.load(f, allow_pickle=True) as data:\n", | |
| " # np.savez saves dicts as object arrays, need .item() to recover\n", | |
| " histories = data['histories'].item()\n", | |
| " \n", | |
| " # Initialize structure on first pass\n", | |
| " if test_dims is None:\n", | |
| " test_dims = data['test_dims']\n", | |
| " for d in test_dims:\n", | |
| " raw_data[d] = {'steps': [], 'L': [], 'beta': []}\n", | |
| "\n", | |
| " # Extract runs\n", | |
| " for d in test_dims:\n", | |
| " # Ensure this dimension exists in this specific run\n", | |
| " if d in histories and len(histories[d]['steps']) > 0:\n", | |
| " raw_data[d]['steps'].append(np.array(histories[d]['steps']))\n", | |
| " raw_data[d]['L'].append(np.array(histories[d]['L']))\n", | |
| " raw_data[d]['beta'].append(np.array(histories[d]['beta']))\n", | |
| "\n", | |
| " # 4. Process and Align Data\n", | |
| " # We need a common X-axis (time steps) to compute the average at every point\n", | |
| " processed_stats = {}\n", | |
| " \n", | |
| " for d in test_dims:\n", | |
| " runs_steps = raw_data[d]['steps']\n", | |
| " runs_L = raw_data[d]['L']\n", | |
| " runs_beta = raw_data[d]['beta']\n", | |
| " \n", | |
| " if not runs_steps:\n", | |
| " continue\n", | |
| "\n", | |
| " # Create a common grid from 0 to the maximum step observed in any run\n", | |
| " max_step = max(s[-1] for s in runs_steps)\n", | |
| " # Assuming all runs used roughly the same interval, pick the finest resolution\n", | |
| " min_step_diff = min(np.diff(s).min() for s in runs_steps if len(s) > 1)\n", | |
| " common_grid = np.arange(0, max_step + 1, min_step_diff)\n", | |
| "\n", | |
| " # Matrix to store interpolated values: shape (num_runs, len(common_grid))\n", | |
| " # Fill with NaN so short runs don't drag down the average of long runs\n", | |
| " L_matrix = np.full((len(runs_L), len(common_grid)), np.nan)\n", | |
| " beta_matrix = np.full((len(runs_beta), len(common_grid)), np.nan)\n", | |
| "\n", | |
| " for i, (s, l, b) in enumerate(zip(runs_steps, runs_L, runs_beta)):\n", | |
| " # Interpolate onto common grid\n", | |
| " # Values outside the run's range remain NaN\n", | |
| " valid_indices = (common_grid >= s[0]) & (common_grid <= s[-1])\n", | |
| " \n", | |
| " L_matrix[i, valid_indices] = np.interp(common_grid[valid_indices], s, l)\n", | |
| " beta_matrix[i, valid_indices] = np.interp(common_grid[valid_indices], s, b)\n", | |
| "\n", | |
| " # Compute Statistics (ignoring NaNs)\n", | |
| " with warnings.catch_warnings():\n", | |
| " warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n", | |
| " \n", | |
| " mean_L = np.nanmean(L_matrix, axis=0)\n", | |
| " std_L = np.nanstd(L_matrix, axis=0)\n", | |
| " mean_beta = np.nanmean(beta_matrix, axis=0)\n", | |
| " \n", | |
| " # Count how many runs are active at each point\n", | |
| " active_runs = np.sum(~np.isnan(L_matrix), axis=0)\n", | |
| "\n", | |
| " processed_stats[d] = {\n", | |
| " 'steps': common_grid,\n", | |
| " 'mean_L': mean_L,\n", | |
| " 'std_L': std_L,\n", | |
| " 'mean_beta': mean_beta,\n", | |
| " 'active_runs': active_runs\n", | |
| " }\n", | |
| "\n", | |
| " # 5. Plotting\n", | |
| " plt.figure(figsize=(12, 7))\n", | |
| " colormap = plt.cm.get_cmap('viridis', len(test_dims))\n", | |
| " colors = {d: colormap(i) for i, d in enumerate(test_dims)}\n", | |
| "\n", | |
| " # Plot Threshold (Use the first D as reference, or average)\n", | |
| " ref_d = test_dims[0]\n", | |
| " if ref_d in processed_stats:\n", | |
| " # Plot only where we have valid data\n", | |
| " valid_mask = ~np.isnan(processed_stats[ref_d]['mean_beta'])\n", | |
| " plt.plot(processed_stats[ref_d]['steps'][valid_mask], \n", | |
| " processed_stats[ref_d]['mean_beta'][valid_mask],\n", | |
| " linestyle='--', color='black', label=r'Threshold ($\\beta_t$)', linewidth=2)\n", | |
| "\n", | |
| " for d in test_dims:\n", | |
| " if d not in processed_stats: continue\n", | |
| " \n", | |
| " stats = processed_stats[d]\n", | |
| " steps = stats['steps']\n", | |
| " mean = stats['mean_L']\n", | |
| " std = stats['std_L']\n", | |
| " \n", | |
| " # Only plot parts where we have data\n", | |
| " mask = ~np.isnan(mean)\n", | |
| " \n", | |
| " # Plot Mean\n", | |
| " plt.plot(steps[mask], mean[mask], label=f'Statistic (d={d})', color=colors[d], linewidth=2)\n", | |
| " \n", | |
| " # Plot Standard Deviation Shading\n", | |
| " plt.fill_between(steps[mask], \n", | |
| " (mean - 3*std)[mask], \n", | |
| " (mean + 3*std)[mask], \n", | |
| " color=colors[d], alpha=0.2)\n", | |
| "\n", | |
| " plt.title(f\"Average Trajectory over {len(files)} Runs\\n(Shaded Area = 3 Std Dev)\")\n", | |
| " plt.xlabel(\"Time Steps\")\n", | |
| " plt.ylabel(\"Statistic ($L_t$)\")\n", | |
| " plt.legend(fontsize='xx-large')\n", | |
| " plt.grid(True, alpha=0.3)\n", | |
| " \n", | |
| " # Optional: Save the plot\n", | |
| " plt.savefig(os.path.join(folder_name, \"aggregated_plot_final.png\"))\n", | |
| " plt.show()\n", | |
| "\n", | |
| "if __name__ == \"__main__\":\n", | |
| " load_and_plot_aggregated_runs()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.13.1" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } | |
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- Size:
- 278 kB
- Xet hash:
- ace588a0b47d9fb16e2693e49642d4f812d7227a76b54badca26ad1fae0c24fd
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