From 27633e79b8c400cae8d69c75f12760456ba51fa5 Mon Sep 17 00:00:00 2001 From: tbohne Date: Wed, 17 Jan 2024 15:51:19 +0100 Subject: [PATCH 1/3] refine labeling for split plot --- nbs/003_data.validation.ipynb | 1123 ++------------------------------- 1 file changed, 43 insertions(+), 1080 deletions(-) diff --git a/nbs/003_data.validation.ipynb b/nbs/003_data.validation.ipynb index 0ed147a3b..51ccb87c0 100644 --- a/nbs/003_data.validation.ipynb +++ b/nbs/003_data.validation.ipynb @@ -131,17 +131,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "imbalanced: [0 1 2 3 4] [24 43 64 41 8]\n", - "balanced (oversample): [0 1 2 3 4] [64 64 64 64 64]\n", - "balanced (undersample): [0 1 2 3 4] [8 8 8 8 8]\n" - ] - } - ], + "outputs": [], "source": [ "y = np.concatenate([[i] * np.random.randint(10, 100) for i in range(5)])\n", "train_split = np.random.choice(len(y), int(len(y) * .8), False)\n", @@ -345,7 +335,7 @@ "outputs": [], "source": [ "#|export\n", - "def plot_splits(splits):\n", + "def plot_splits(splits, contains_test_data):\n", " _max = 0\n", " _splits = 0\n", " for i, split in enumerate(splits):\n", @@ -364,7 +354,7 @@ " v[i, s] = 1 + j\n", " else: v[i, split] = 1 + i\n", " vals = np.unique(v)\n", - " if 2 in vals and 3 not in vals:\n", + " if 2 in vals and 3 not in vals and contains_test_data:\n", " vals = [v + 1 if v == 2 else v for v in vals]\n", " plt.figure(figsize=(16, len(_splits)/2))\n", " if len(vals) == 1:\n", @@ -459,7 +449,7 @@ " if valid_size != 0: splits[1] = splits[0]\n", " if test_size != 0: splits[2] = splits[0]\n", " splits = tuple(splits)\n", - " if show_plot: plot_splits(splits)\n", + " if show_plot: plot_splits(splits, test_size > 0)\n", " return splits" ] }, @@ -467,42 +457,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "((#256) [550,813,388,595,948,198,354,749,175,812...],\n", - " (#256) [550,813,388,595,948,198,354,749,175,812...])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train_size=256\n", "y = np.random.randint(0, 3, 1000) + 100\n", @@ -661,7 +587,7 @@ " splits.append((L(train_idxs[n]),))\n", " splits = tuple(splits)[::-1]\n", " if show_plot:\n", - " plot_splits(splits)\n", + " plot_splits(splits, test_size > 0)\n", " return splits" ] }, @@ -669,28 +595,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "o = np.random.rand(10_000, 3, 50) # shape: [samples x features x steps]\n", "\n", @@ -764,7 +669,7 @@ " if len(o) > 1_000_000:\n", " warnings.warn('the splits are too large to be plotted')\n", " else: \n", - " plot_splits(splits) if test_size else plot_splits(splits[:2])\n", + " plot_splits(splits, test_size > 0) if test_size else plot_splits(splits[:2], test_size > 0)\n", " return splits\n", " return _inner\n", "\n", @@ -775,59 +680,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "((#800) [0,1,2,3,4,5,6,7,8,9...],\n", - " (#200) [800,801,802,803,804,805,806,807,808,809...])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "y = np.arange(1000) + 100\n", "test_eq(TimeSplitter(valid_size=0.2)(y)[1], L(np.arange(800, 1000).tolist()))\n", @@ -839,35 +692,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stratify set to False as n_splits=5 cannot be greater than the min number of members in each class (1).\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(601.11, (#800) [314,194,782,789,502,917,137,415,904,181...])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "n_splits = 5\n", "valid_size = 0.2 \n", @@ -894,27 +719,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[((#800) [314,194,782,789,502,917,137,415,904,181...],\n", - " (#200) [362,151,934,378,95,597,500,117,980,844...]),\n", - " ((#800) [312,198,777,788,515,910,145,413,898,186...],\n", - " (#200) [352,133,955,396,64,596,442,79,991,882...]),\n", - " ((#800) [311,197,783,791,507,922,145,416,908,184...],\n", - " (#200) [338,125,912,361,54,594,486,88,994,859...]),\n", - " ((#800) [296,181,782,789,493,917,130,401,905,165...],\n", - " (#200) [405,199,953,444,113,610,515,137,997,881...]),\n", - " ((#800) [320,190,782,788,506,906,141,412,893,178...],\n", - " (#200) [336,149,942,358,49,582,472,70,990,907...])]" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "list([splits[0], splits[1], splits[2], splits[3], splits[4]])" ] @@ -923,26 +728,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stratify set to False as n_splits=5 cannot be greater than the min number of members in each class (1).\n", - "valid == train\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_splits = 5\n", "valid_size = 0.\n", @@ -966,28 +752,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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a95LNNU68cpbW1q61AwAAAAAAoO2RXAYAAAAAIDXJZQAAAAAAUmvxdy4DAAAAADSH2tra2LhxY2uH0WZ16NAhSkvr+72g+kkuAwAAAABt3saNG+P111+P2tra1g6lTausrIzevXvn9YORkssAAAAAQJuWJEmsXr06SktLo1+/ftGunbcBp5UkSaxfvz7WrFkTERF9+vRpdBvJZQAAAACgTdu8eXOsX78++vbtG126dGntcNqszp07R0TEmjVrYtddd230FRlS+AAAAABAm1ZTUxMRER07dmzlSNq+Lcn5TZs2NVpWchkAAAAA2CHk855gGpamDyWXAQAAAABITXIZAAAAAGAHMXDgwLjssstapC3JZQAAAABgh1RS0rJLuthKGlxmzJhR0D4vWrQoTjnllIK2TSt1cnnhwoVxzDHHRN++faOkpCR+97vfNUNYAAAAAAA7rtWrV2eWyy67LMrLy7PWnXPOOZmySZLE5s2b86q3Z8+emR/la26pk8vvvfde7L///nHVVVc1RzwAAAAAADu83r17Z5aKioooKSnJfH7ppZeie/fucffdd8fIkSOjrKwsHnnkkXj11Vfj2GOPjV69ekW3bt1i1KhRcf/992fVW/e1GCUlJfGLX/wijj/++OjSpUsMGTIk7rjjjqLsQ+rk8oQJE+LHP/5xHH/88UUJAAAAAACAbX3ve9+Liy66KF588cUYPnx4rFu3Lj796U/HAw88EE8//XSMHz8+jjnmmFi5cmWD9cycOTM+//nPx7PPPhuf/vSnY+LEifH22283Ob5mf+fyhg0borq6OmsBAAAAAKBh559/fhx11FExePDg6NGjR+y///7xzW9+M4YNGxZDhgyJH/3oRzF48OBGn0SePHlynHjiifGxj30sLrzwwli3bl08+eSTTY6v2ZPLs2bNioqKiszSr1+/5m4SAAAAAKDNO/DAA7M+r1u3Ls4555zYe++9o7KyMrp16xYvvvhio08uDx8+PPPvrl27Rnl5eaxZs6bJ8TV7cnnq1KlRVVWVWVatWtXcTQIAAAAAtHldu3bN+nzOOefEbbfdFhdeeGE8/PDDsXTp0thvv/1i48aNDdbToUOHrM8lJSVRW1vb5PjaN7mGRpSVlUVZWVlzNwMAAAAAsEP705/+FJMnT878Ht66detixYoVrRZPsz+5DAAAAABA0w0ZMiRuvfXWWLp0aTzzzDNx0kknFeUJ5EKlfnJ53bp18ec//znz+fXXX4+lS5dGjx49on///kUNDgAAAACgUEnS2hEU189+9rP46le/GgcffHDssssucd5550V1dXWrxZM6ubx48eL41Kc+lfl89tlnR0TEpEmTYu7cuUULDAAAAADgo2Dy5MkxefLkzOfDDz88knoy4wMHDowHH3wwa92UKVOyPtd9TUZ99axdu7bgWLeWOrmca8cAAAAAAPjo8M5lAAAAAABSk1wGAAAAACA1yWUAAAAAAFKTXAYAAAAAIDXJZQAAAAAAUpNcBgAAAAAgNcllAAAAAABSk1wGAAAAACA1yWUAAAAAAFJr39oBAAAAAAA0h5FPjWzR9paMWJJ32ZKSkga/nz59esyYMaOgOEpKSuK2226L4447rqDt8yW5DAAAAADQwlavXp3598033xzTpk2L5cuXZ9Z169atNcJKxWsxAAAAAABaWO/evTNLRUVFlJSUZK2bN29e7L333tGpU6fYa6+94uqrr85su3HjxjjttNOiT58+0alTpxgwYEDMmjUrIiIGDhwYERHHH398lJSUZD43i6SFVVVVJRGR7L9g/yQiyVpGLBmxzbp8l623HbFkRKN1NbWtpmzfWB1NrTtN28Xoh/rqaOi7tPvfWH+nPR5px0Y+dRdj7Nbdj7RjutD9yLfeXMe50P0uZh1Nrb/ueG1s/BajP9KMq1xzothjP98xVMiczyeGYpxb08Td0H7kM07q26bY4zntOMunf/PZv1znpXzHUnPO6zTnyULO58WMv6HzenP3TaFjqxj3N821XTGPS5rjlk9/NcexTXOeL9b5sxjHp9Bjmesa3JQ5lO84z/faVOj9WLHGSVPuPdJs21Cf53MtKfY4a67zSpr7luacYw1da5vS341d74sVe906G1qXJu585059fdTY/Vo+deTTV7naLeS8lc8cbWwfCynX2Pk43/1Ic83KZ/u093hp+jWfsvn0b77jJt/Y8xl/2277z/xaVVVVS6f2tlvvv/9+smzZsuT999/PWl/3nNrcS6Guv/76pKKiIvP5hhtuSPr06ZP89re/TV577bXkt7/9bdKjR49k7ty5SZIkyaWXXpr069cvWbhwYbJixYrk4YcfTm666aYkSZJkzZo1SUQk119/fbJ69epkzZo1RenL+ngtBgAAAADAdmT69Onx05/+NE444YSIiBg0aFAsW7Ys5syZE5MmTYqVK1fGkCFD4tBDD42SkpIYMGBAZtuePXtGRERlZWX07t27WeOUXAYAAAAA2E6899578eqrr8bXvva1+MY3vpFZv3nz5qioqIiIiMmTJ8dRRx0VQ4cOjfHjx8d//Md/xNixY1s8VsllAAAAAIDtxLp16yIi4rrrrovRo0dnfVdaWhoRESNGjIjXX3897r777rj//vvj85//fBx55JHxm9/8pkVjlVwGAAAAANhO9OrVK/r27RuvvfZaTJw4MWe58vLy+MIXvhBf+MIX4rOf/WyMHz8+3n777ejRo0d06NAhampqmj1WyWUAAAAAgO3IzJkz44wzzoiKiooYP358bNiwIRYvXhzvvPNOnH322fGzn/0s+vTpEwcccEC0a9cubrnllujdu3dUVlZGRMTAgQPjgQceiEMOOSTKyspip512apY4JZcBAAAAgB3SkhFLWjuEgnz961+PLl26xKWXXhrnnntudO3aNfbbb78466yzIiKie/fucckll8Qrr7wSpaWlMWrUqLjrrruiXbt2ERHx05/+NM4+++y47rrrYrfddosVK1Y0S5ySywAAAAAArWjy5MkxefLkrHUnnXRSnHTSSfWW/8Y3vpH1Y391HXPMMXHMMccUM8R6tWv2FgAAAAAA2OFILgMAAAAAkJrkMgAAAAAAqUkuAwAAAACQmuQyAAAAALBDSJKktUNo89L0oeQyAAAAANCmlZaWRkTExo0bWzmStm/9+vUREdGhQ4dGy7Zv7mAAAAAAAJpT+/bto0uXLvHWW29Fhw4dol07z9SmlSRJrF+/PtasWROVlZWZhH1DJJcBAAAAgDatpKQk+vTpE6+//nr85S9/ae1w2rTKysro3bt3XmVTJZdnzZoVt956a7z00kvRuXPnOPjgg+Piiy+OoUOHFhQoAAAAAEAxdOzYMYYMGeLVGE3QoUOHvJ5Y3iJVcnnBggUxZcqUGDVqVGzevDm+//3vx9ixY2PZsmXRtWvX1MECAAAAABRLu3btolOnTq0dxkdGquTyPffck/V57ty5seuuu8aSJUviE5/4RFEDAwAAAABg+9Wkdy5XVVVFRESPHj1yltmwYUNs2LAh87m6uropTQIAAAAAsB0o+GcTa2tr46yzzopDDjkkhg0blrPcrFmzoqKiIrP069ev0CYBAAAAANhOFJxcnjJlSjz//PMxb968BstNnTo1qqqqMsuqVasKbRIAAAAAgO1EQa/FOO200+LOO++MhQsXxu67795g2bKysigrKysoOAAAAAAAtk+pkstJksTpp58et912Wzz00EMxaNCg5ooLAAAAAIDtWKrk8pQpU+Kmm26K22+/Pbp37x5vvvlmRERUVFRE586dmyVAAAAAAAC2P6neuTx79uyoqqqKww8/PPr06ZNZbr755uaKDwAAAACA7VDq12IAAAAAAECqJ5cBAAAAACBCchkAAAAAgAJILgMAAAAAkJrkMgAAAAAAqUkuAwAAAACQmuQyAAAAAACpSS4DAAAAAJCa5DIAAAAAAKlJLgMAAAAAkJrkMgAAAAAAqUkuAwAAAACQmuQyAAAAAADpJS2sqqoqiYgkoiqJSFp8GbFkRM51I5aMyCz5fJdve1tvl3b7xuJvqL6G9rUp7aTth7QxFvMYN1Z/sY9PU/ujWMerWP2dq3zdPiuk3+obY801VnLFWl+bzTFHCz1vtPa4KrSuXPMpzTjN1Qf5zu+058pc5/5i7Hfa457P9luXbWhcF2Pe1K0zbX/k206xxmVD561CY2zsGBV6j9BQ/9Z3HBs73mmOZ1PjTtOf9Y3VxvoybRz5xNOUY5TPeCl0bDT3vVDa/sl1Lsx1zsnnmDf3/VWuOPM5NsVoL23/1FdXIXO5sWOZdqw1FEOa+d0cxzZtnxZyvOs7T+Wz700dT2mOQ6H7Xcg5u5jjJO3xLrTOXOewYtwDpN2uOc9BhcTV2HgtZL8KjbM55kuua1facVPoubglrnX1xbD/gv2TiEiqqqpaOrUHWTy5DAAAAABAapLLAAAAAACkJrkMAAAAAEBqkssAAAAAAKQmuQwAAAAAQGqSywAAAAAApCa5DAAAAABAapLLAAAAAACkJrkMAAAAAEBqkssAAAAAAKQmuQwAAAAAQGqSywAAAAAApCa5DAAAAABAapLLAAAAAACkliq5PHv27Bg+fHiUl5dHeXl5jBkzJu6+++7mig0AAAAAgO1UquTy7rvvHhdddFEsWbIkFi9eHP/+7/8exx57bLzwwgvNFR8AAAAAANuh9mkKH3PMMVmfL7jggpg9e3Y8/vjjse+++xY1MAAAAAAAtl+pkstbq6mpiVtuuSXee++9GDNmTM5yGzZsiA0bNmQ+V1dXF9okAAAAAADbidQ/6Pfcc89Ft27doqysLL71rW/FbbfdFvvss0/O8rNmzYqKiorM0q9fvyYFDAAAAABA60udXB46dGgsXbo0nnjiifj2t78dkyZNimXLluUsP3Xq1Kiqqsosq1atalLAAAAAAAC0vtSvxejYsWN87GMfi4iIkSNHxqJFi+LnP/95zJkzp97yZWVlUVZW1rQoAQAAAADYrqR+crmu2trarHcqAwAAAACw40v15PLUqVNjwoQJ0b9//3j33Xfjpptuioceeijuvffe5ooPAAAAAIDtUKrk8po1a+IrX/lKrF69OioqKmL48OFx7733xlFHHdVc8QEAAAAAsB1KlVz+7//+7+aKAwAAAACANqTJ71wGAAAAAOCjR3IZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA1CSXAQAAAABITXIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA9JIWVlVVlUREElGVRCTJiCUjkhFLRiQRSdbnrZct67eUaWjJVa5uO/lul3apL/ZC4i5k27RtptmfpvZTQ/2SK+6G9qehupqrf/Lti3yPf7GOV31zpLF5k2veFXPspB0D+exPS8Waq4/zjTFtfWnqaOw8Vkj/NNS/hZ6b0myXts6614ZCz/v59mcxxk9T4sw3jqYc96ZsU8ixzacvch3vYh+npl4jmnJs8r0e5lNvMc+LaY9DscZePuOrkPNFvvuYq0xz31s1ta20Y6ap464l4841P5tyHW2u/WksnnyvtfneKzX1mBWyH8Xq34bW5Xtea6yPGlrf1HNYmnGX9rrZ1PYKaWPrMddcc6ux41PIPhV6T5qmT5oaS1Oua3XPf8W6BhcaZ0P9k++8zhV7mrHRlHGa5rzR0LEoZLzl2ra+uZfPNe/Ddf/Mr1VVVbV0ag+yeHIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA1CSXAQAAAABITXIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA1CSXAQAAAABITXIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNSalFy+6KKLoqSkJM4666wihQMAAAAAQFtQcHJ50aJFMWfOnBg+fHgx4wEAAAAAoA0oKLm8bt26mDhxYlx33XWx0047FTsmAAAAAAC2cwUll6dMmRJHH310HHnkkY2W3bBhQ1RXV2ctAAAAAAC0be3TbjBv3rx46qmnYtGiRXmVnzVrVsycOTN1YAAAAAAAbL9SPbm8atWqOPPMM+PGG2+MTp065bXN1KlTo6qqKrOsWrWqoEABAAAAANh+pHpyecmSJbFmzZoYMWJEZl1NTU0sXLgwrrzyytiwYUOUlpZmbVNWVhZlZWXFiRYAAAAAgO1CquTyEUccEc8991zWupNPPjn22muvOO+887ZJLAMAAAAAsGNKlVzu3r17DBs2LGtd165dY+edd95mPQAAAAAAO65U71wGAAAAAICIlE8u1+ehhx4qQhgAAAAAALQlnlwGAAAAACA1yWUAAAAAAFKTXAYAAAAAIDXJZQAAAAAAUpNcBgAAAAAgNcllAAAAAABSk1wGAAAAACA1yWUAAAAAAFKTXAYAAAAAIDXJZQAAAAAAUpNcBgAAAAAgNcllAAAAAADSS1pYVVVVEhFJRFUyYsmIJCLJexmxZETWsvW6xrZL007apW5MjcVRt1yufWmozrTf5VqXpn9aqh8LbauhbfKpr76+b6iP8+33pvRDY+Mt3+Ncd99yxdfY9i013/Lp91yxpD3WxRhfDR2jxtbl224+fZ9mP5o6VvONp6ExlaYvinmeKqQv853zzbmkba+hsdOUuAsZk/leJ4sRX93Y0s7FXGXzOV829z4VWjafY1bo+bSx7Yt13srnfFGMcV2spSljvaHrW0NjMk3/5HPObYnrfVP6Nt/zSmP73dg9V7HHTzH7LZ9zWz73lYWOgbTHK01c+cbU2PmhofNhIccpn/nXUIyNHYt8vy9W/zVlzBWyTUN9Ucg9XDHvF1qy/xq7Tjd1fBajb/KtszXGVL593NC9YFOPWVP3r6Frfbrj8M/8WlVVVUun9iCLJ5cBAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA1CSXAQAAAABITXIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA1CSXAQAAAABITXIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNRSJZdnzJgRJSUlWctee+3VXLEBAAAAALCdap92g3333Tfuv//+Dyton7oKAAAAAADauNSZ4fbt20fv3r2bIxYAAAAAANqI1O9cfuWVV6Jv376xxx57xMSJE2PlypUNlt+wYUNUV1dnLQAAAAAAtG2pksujR4+OuXPnxj333BOzZ8+O119/PQ477LB49913c24za9asqKioyCz9+vVrctAAAAAAALSuVMnlCRMmxOc+97kYPnx4jBs3Lu66665Yu3Zt/PrXv865zdSpU6OqqiqzrFq1qslBAwAAAADQupr0a3yVlZWx5557xp///OecZcrKyqKsrKwpzQAAAAAAsJ1J/c7lra1bty5effXV6NOnT7HiAQAAAACgDUiVXD7nnHNiwYIFsWLFinj00Ufj+OOPj9LS0jjxxBObKz4AAAAAALZDqV6L8de//jVOPPHE+Mc//hE9e/aMQw89NB5//PHo2bNnc8UHAAAAAMB2KFVyed68ec0VBwAAAAAAbUiT3rkMAAAAAMBHk+QyAAAAAACpSS4DAAAAAJCa5DIAAAAAAKlJLgMAAAAAkJrkMgAAAAAAqUkuAwAAAACQmuQyAAAAAACpSS4DAAAAAJCa5DIAAAAAAKlJLgMAAAAAkFr7lm4wSZJ//as6atbVRER13tv+s/zWqrdal7uetO2klR1X43HUjSd7/Yd1NLRvab/LvS7/49By/Zh+bHy4ff3b5FPf1mUaO6b11ddQH6fR2Jiubx40HsOH67bdz/y3b6n5lquO+o9Rfcch/2Pd1Jhyl90i95hKE2s+fZ9/bLnPQ+nqaTyehsfUh+sba7+Y56lC+jLfOd+c0rbX0NhpyjwtZEzme52s20ahGjpvNTb+cl8LPixft438Y2rqPhXWVj7HLPcYL3z/inve+rCefK4RhbVRvLnclHNWw9e3uvLvh4au//WXrb/+xtppbtuO52gwjsb2u6n3j2nKpS2bpq587z3yjac57zvSXhMaiqmx80PD58P04zu/vxFyx9j4eKu/3vrryV22kPrzr6Pwe+iG+qKQe7hi3i8Us2zDdeSTD2i4nXzmbbHONY3V2ZL3N2nqa/xesJD76fRxpj3Hpj+2//zvh3k2aB0lSQuPwtdeey0GDx7ckk0CAAAAwA7n1VdfjT322KO1w+AjrMWfXO7Ro0dERKxcuTIqKipaunmgGVVXV0e/fv1i1apVUV5e3trhAEVkfsOOy/yGHZf5DTuuqqqq6N+/fybPBq2lxZPL7dq1i4iIiooKFzfYQZWXl5vfsIMyv2HHZX7Djsv8hh3XljwbtBYjEAAAAACA1CSXAQAAAABIrcWTy2VlZTF9+vQoKytr6aaBZmZ+w47L/IYdl/kNOy7zG3Zc5jfbi5IkSZLWDgIAAAAAgLbFazEAAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA1CSXAQAAAABIrUWTy1dddVUMHDgwOnXqFKNHj44nn3yyJZsHUpo1a1aMGjUqunfvHrvuumscd9xxsXz58qwyH3zwQUyZMiV23nnn6NatW3zmM5+Jv//971llVq5cGUcffXR06dIldt111zj33HNj8+bNLbkrQCMuuuiiKCkpibPOOiuzzvyGtutvf/tbfOlLX4qdd945OnfuHPvtt18sXrw4832SJDFt2rTo06dPdO7cOY488sh45ZVXsup4++23Y+LEiVFeXh6VlZXxta99LdatW9fSuwJspaamJn74wx/GoEGDonPnzjF48OD40Y9+FEmSZMqY39B2LFy4MI455pjo27dvlJSUxO9+97us74s1n5999tk47LDDolOnTtGvX7+45JJLmnvX+AhpseTyzTffHGeffXZMnz49nnrqqdh///1j3LhxsWbNmpYKAUhpwYIFMWXKlHj88cdj/vz5sWnTphg7dmy89957mTLf+c534ve//33ccsstsWDBgnjjjTfihBNOyHxfU1MTRx99dGzcuDEeffTR+OUvfxlz586NadOmtcYuAfVYtGhRzJkzJ4YPH5613vyGtumdd96JQw45JDp06BB33313LFu2LH7605/GTjvtlClzySWXxOWXXx7XXHNNPPHEE9G1a9cYN25cfPDBB5kyEydOjBdeeCHmz58fd955ZyxcuDBOOeWU1tgl4F8uvvjimD17dlx55ZXx4osvxsUXXxyXXHJJXHHFFZky5je0He+9917sv//+cdVVV9X7fTHmc3V1dYwdOzYGDBgQS5YsiUsvvTRmzJgR1157bbPvHx8RSQs56KCDkilTpmQ+19TUJH379k1mzZrVUiEATbRmzZokIpIFCxYkSZIka9euTTp06JDccsstmTIvvvhiEhHJY489liRJktx1111Ju3btkjfffDNTZvbs2Ul5eXmyYcOGlt0BYBvvvvtuMmTIkGT+/PnJJz/5yeTMM89MksT8hrbsvPPOSw499NCc39fW1ia9e/dOLr300sy6tWvXJmVlZcmvfvWrJEmSZNmyZUlEJIsWLcqUufvuu5OSkpLkb3/7W/MFDzTo6KOPTr761a9mrTvhhBOSiRMnJklifkNbFhHJbbfdlvlcrPl89dVXJzvttFPW/fl5552XDB06tJn3iI+KFnlyeePGjbFkyZI48sgjM+vatWsXRx55ZDz22GMtEQJQBFVVVRER0aNHj4iIWLJkSWzatClrbu+1117Rv3//zNx+7LHHYr/99otevXplyowbNy6qq6vjhRdeaMHogfpMmTIljj766Kx5HGF+Q1t2xx13xIEHHhif+9znYtddd40DDjggrrvuusz3r7/+erz55ptZ87uioiJGjx6dNb8rKyvjwAMPzJQ58sgjo127dvHEE0+03M4AWQ4++OB44IEH4uWXX46IiGeeeSYeeeSRmDBhQkSY37AjKdZ8fuyxx+ITn/hEdOzYMVNm3LhxsXz58njnnXdaaG/YkbVviUb+7//+L2pqarL++IyI6NWrV7z00kstEQLQRLW1tXHWWWfFIYccEsOGDYuIiDfffDM6duwYlZWVWWV79eoVb775ZqZMfXN/y3dA65k3b1489dRTsWjRom2+M7+h7Xrttddi9uzZcfbZZ8f3v//9WLRoUZxxxhnRsWPHmDRpUmZ+1jd/t57fu+66a9b37du3jx49epjf0Iq+973vRXV1dey1115RWloaNTU1ccEFF8TEiRMjIsxv2IEUaz6/+eabMWjQoG3q2PLd1q/NgkK0SHIZaPumTJkSzz//fDzyyCOtHQpQBKtWrYozzzwz5s+fH506dWrtcIAiqq2tjQMPPDAuvPDCiIg44IAD4vnnn49rrrkmJk2a1MrRAU3x61//Om688ca46aabYt99942lS5fGWWedFX379jW/AWgVLfJajF122SVKS0u3+YX5v//979G7d++WCAFogtNOOy3uvPPO+OMf/xi77757Zn3v3r1j48aNsXbt2qzyW8/t3r171zv3t3wHtI4lS5bEmjVrYsSIEdG+ffto3759LFiwIC6//PJo37599OrVy/yGNqpPnz6xzz77ZK3be++9Y+XKlRHx4fxs6N68d+/e2/zw9ubNm+Ptt982v6EVnXvuufG9730vvvjFL8Z+++0XX/7yl+M73/lOzJo1KyLMb9iRFGs+u2enubVIcrljx44xcuTIeOCBBzLramtr44EHHogxY8a0RAhAAZIkidNOOy1uu+22ePDBB7f5X2lGjhwZHTp0yJrby5cvj5UrV2bm9pgxY+K5557LuuDNnz8/ysvLt/nDF2g5RxxxRDz33HOxdOnSzHLggQfGxIkTM/82v6FtOuSQQ2L58uVZ615++eUYMGBAREQMGjQoevfunTW/q6ur44knnsia32vXro0lS5Zkyjz44INRW1sbo0ePboG9AOqzfv36aNcu+8/40tLSqK2tjQjzG3YkxZrPY8aMiYULF8amTZsyZebPnx9Dhw71SgyKo6V+OXDevHlJWVlZMnfu3GTZsmXJKaecklRWVmb9wjywffn2t7+dVFRUJA899FCyevXqzLJ+/fpMmW9961tJ//79kwcffDBZvHhxMmbMmGTMmDGZ7zdv3pwMGzYsGTt2bLJ06dLknnvuSXr27JlMnTq1NXYJaMAnP/nJ5Mwzz8x8Nr+hbXryySeT9u3bJxdccEHyyiuvJDfeeGPSpUuX5IYbbsiUueiii5LKysrk9ttvT5599tnk2GOPTQYNGpS8//77mTLjx49PDjjggOSJJ55IHnnkkWTIkCHJiSee2Bq7BPzLpEmTkt122y258847k9dffz259dZbk1122SX57ne/myljfkPb8e677yZPP/108vTTTycRkfzsZz9Lnn766eQvf/lLkiTFmc9r165NevXqlXz5y19Onn/++WTevHlJly5dkjlz5rT4/rJjarHkcpIkyRVXXJH0798/6dixY3LQQQcljz/+eEs2D6QUEfUu119/fabM+++/n5x66qnJTjvtlHTp0iU5/vjjk9WrV2fVs2LFimTChAlJ586dk1122SX5r//6r2TTpk0tvDdAY+oml81vaLt+//vfJ8OGDUvKysqSvfbaK7n22muzvq+trU1++MMfJr169UrKysqSI444Ilm+fHlWmX/84x/JiSeemHTr1i0pLy9PTj755OTdd99tyd0A6qiurk7OPPPMpH///kmnTp2SPfbYI/l//+//JRs2bMiUMb+h7fjjH/9Y79/ckyZNSpKkePP5mWeeSQ499NCkrKws2W233ZKLLrqopXaRj4CSJEmS1nlmGgAAAACAtqpF3rkMAAAAAMCORXIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNQklwEAAAAASE1yGQAAAACA1CSXAQAAAABITXIZAAAAAIDUJJcBAAAAAEhNchkAAAAAgNQklwEAAAAASO3/A+Z3aqrKEk9/AAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_splits = 2\n", "y = np.random.randint(0, 2, 1000)\n", @@ -1035,48 +789,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "y = np.random.randint(0, 2, 1000)\n", "splits1 = get_splits(y, valid_size=.25, test_size=0, random_state=23, stratify=True, shuffle=True)\n", @@ -1091,18 +804,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "y = np.random.randint(0, 2, 100)\n", "splits = get_splits(y, valid_size=.25, test_size=0, random_state=23, stratify=True, shuffle=True)\n", @@ -1113,18 +815,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "y = np.random.randint(0, 2, 100)\n", "splits = get_splits(y, valid_size=.25, test_size=0, random_state=23, stratify=True)\n", @@ -1135,18 +826,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "y = np.random.randint(0, 2, 100)\n", "splits = get_splits(y, valid_size=.25, test_size=20, random_state=23, stratify=True)\n", @@ -1169,58 +849,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "o = np.random.randint(0, 2, 1000)\n", "for p in [1, .75, .5, .25, .125]:\n", @@ -1232,30 +861,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "((#60) [58,95,53,44,28,69,9,12,22,88...],\n", - " (#20) [89,71,60,4,19,37,75,13,46,30...],\n", - " (#20) [76,68,74,29,16,97,14,21,90,82...])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "y = L([0] * 50 + [1] * 25 + [2] * 15 + [3] * 10)\n", "splits = get_splits(y, valid_size=.2, test_size=.2)\n", @@ -1267,30 +873,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "((#60) [0,1,2,3,4,5,6,7,8,9...],\n", - " (#20) [60,61,62,63,64,65,66,67,68,69...],\n", - " (#20) [80,81,82,83,84,85,86,87,88,89...])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "y = L([0] * 50 + [1] * 25 + [2] * 15 + [3] * 10)\n", "splits = get_splits(y, n_splits=1, valid_size=.2, test_size=.2, shuffle=False)\n", @@ -1302,18 +885,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "splits = get_splits(np.random.randint(0,5,100), valid_size=0.213, test_size=17)\n", "test_eq(len(splits[1]), 21)\n", @@ -1324,30 +896,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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@@ -2530,9 +1481,21 @@ ], "metadata": { "kernelspec": { - "display_name": "python3", + "display_name": "Python 3 (ipykernel)", "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": "3.8.10" } }, "nbformat": 4, From 58061de8850d67cc23554bf70babe4a2e906f01b Mon Sep 17 00:00:00 2001 From: tbohne Date: Wed, 17 Jan 2024 16:07:02 +0100 Subject: [PATCH 2/3] regenerate output + notebook to script conversion --- nbs/003_data.validation.ipynb | 1260 +++++++++++++++++++++++++++++++-- tsai/data/validation.py | 12 +- 2 files changed, 1196 insertions(+), 76 deletions(-) diff --git a/nbs/003_data.validation.ipynb b/nbs/003_data.validation.ipynb index 51ccb87c0..727372ab9 100644 --- a/nbs/003_data.validation.ipynb +++ b/nbs/003_data.validation.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -129,9 +129,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imbalanced: [0 1 2 3 4] [26 60 27 73 54]\n", + "balanced (oversample): [0 1 2 3 4] [73 73 73 73 73]\n", + "balanced (undersample): [0 1 2 3 4] [26 26 26 26 26]\n" + ] + } + ], "source": [ "y = np.concatenate([[i] * np.random.randint(10, 100) for i in range(5)])\n", "train_split = np.random.choice(len(y), int(len(y) * .8), False)\n", @@ -151,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -187,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -380,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -455,9 +465,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "((#800) [0,1,2,3,4,5,6,7,8,9...],\n", + " (#200) [800,801,802,803,804,805,806,807,808,809...])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y = np.arange(1000) + 100\n", "test_eq(TimeSplitter(valid_size=0.2)(y)[1], L(np.arange(800, 1000).tolist()))\n", @@ -690,9 +853,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stratify set to False as n_splits=5 cannot be greater than the min number of members in each class (1).\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(601.11, (#800) [314,194,782,789,502,917,137,415,904,181...])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "n_splits = 5\n", "valid_size = 0.2 \n", @@ -717,18 +910,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[((#800) [314,194,782,789,502,917,137,415,904,181...],\n", + " (#200) [362,151,934,378,95,597,500,117,980,844...]),\n", + " ((#800) [312,198,777,788,515,910,145,413,898,186...],\n", + " (#200) [352,133,955,396,64,596,442,79,991,882...]),\n", + " ((#800) [311,197,783,791,507,922,145,416,908,184...],\n", + " (#200) [338,125,912,361,54,594,486,88,994,859...]),\n", + " ((#800) [296,181,782,789,493,917,130,401,905,165...],\n", + " (#200) [405,199,953,444,113,610,515,137,997,881...]),\n", + " ((#800) [320,190,782,788,506,906,141,412,893,178...],\n", + " (#200) [336,149,942,358,49,582,472,70,990,907...])]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "list([splits[0], splits[1], splits[2], splits[3], splits[4]])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stratify set to False as n_splits=5 cannot be greater than the min number of members in each class (1).\n", + "valid == train\n" + ] + }, + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "y = np.random.randint(0, 2, 1000)\n", "splits1 = get_splits(y, valid_size=.25, test_size=0, random_state=23, stratify=True, shuffle=True)\n", @@ -802,9 +1123,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "y = np.random.randint(0, 2, 100)\n", "splits = get_splits(y, valid_size=.25, test_size=0, random_state=23, stratify=True, shuffle=True)\n", @@ -813,9 +1147,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "y = np.random.randint(0, 2, 100)\n", "splits = get_splits(y, valid_size=.25, test_size=0, random_state=23, stratify=True)\n", @@ -824,9 +1171,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "y = np.random.randint(0, 2, 100)\n", "splits = get_splits(y, valid_size=.25, test_size=20, random_state=23, stratify=True)\n", @@ -836,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -847,9 +1207,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "((#60) [28,4,9,41,88,92,27,52,89,10...],\n", + " (#20) [42,90,8,71,55,75,77,69,44,11...],\n", + " (#20) [98,25,39,57,22,64,24,37,17,74...])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y = L([0] * 50 + [1] * 25 + [2] * 15 + [3] * 10)\n", "splits = get_splits(y, valid_size=.2, test_size=.2)\n", @@ -871,9 +1317,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "((#60) [0,1,2,3,4,5,6,7,8,9...],\n", + " (#20) [60,61,62,63,64,65,66,67,68,69...],\n", + " (#20) [80,81,82,83,84,85,86,87,88,89...])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y = L([0] * 50 + [1] * 25 + [2] * 15 + [3] * 10)\n", "splits = get_splits(y, n_splits=1, valid_size=.2, test_size=.2, shuffle=False)\n", @@ -883,9 +1354,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "splits size : [83, 14, 29] (126: [0.66, 0.11, 0.23])\n" + ] + } + ], "source": [ "df1_len = 100\n", "df2_len = 80\n", @@ -1420,7 +2501,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1461,9 +2542,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "IPython.notebook.save_checkpoint();" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "003_data.validation.ipynb saved at 2024-01-17 15:56:10\n", + "Correct notebook to script conversion! 😃\n", + "Wednesday 17/01/24 15:56:13 CET\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#|eval: false\n", "#|hide\n", diff --git a/tsai/data/validation.py b/tsai/data/validation.py index e82c1c28f..b31117750 100644 --- a/tsai/data/validation.py +++ b/tsai/data/validation.py @@ -169,7 +169,7 @@ def _inner(o, **kwargs): return _inner # %% ../../nbs/003_data.validation.ipynb 13 -def plot_splits(splits): +def plot_splits(splits, contains_test_data): _max = 0 _splits = 0 for i, split in enumerate(splits): @@ -188,7 +188,7 @@ def plot_splits(splits): v[i, s] = 1 + j else: v[i, split] = 1 + i vals = np.unique(v) - if 2 in vals and 3 not in vals: + if 2 in vals and 3 not in vals and contains_test_data: vals = [v + 1 if v == 2 else v for v in vals] plt.figure(figsize=(16, len(_splits)/2)) if len(vals) == 1: @@ -276,7 +276,7 @@ def get_splits(o, n_splits:int=1, valid_size:float=0.2, test_size:float=0., trai if valid_size != 0: splits[1] = splits[0] if test_size != 0: splits[2] = splits[0] splits = tuple(splits) - if show_plot: plot_splits(splits) + if show_plot: plot_splits(splits, test_size > 0) return splits # %% ../../nbs/003_data.validation.ipynb 17 @@ -365,7 +365,7 @@ def get_walk_forward_splits( splits.append((L(train_idxs[n]),)) splits = tuple(splits)[::-1] if show_plot: - plot_splits(splits) + plot_splits(splits, test_size > 0) return splits # %% ../../nbs/003_data.validation.ipynb 19 @@ -405,7 +405,7 @@ def _inner(o): if len(o) > 1_000_000: warnings.warn('the splits are too large to be plotted') else: - plot_splits(splits) if test_size else plot_splits(splits[:2]) + plot_splits(splits, test_size > 0) if test_size else plot_splits(splits[:2], test_size > 0) return splits return _inner @@ -636,7 +636,7 @@ def get_forecasting_splits( if len(df) > 1_000_000: warnings.warn('the splits are too large to be plotted') else: - plot_splits(splits) + plot_splits(splits, test_size > 0) return tuple(splits) # %% ../../nbs/003_data.validation.ipynb 48 From e5bcc2b49d682934b7b6b37c80ab737038890e07 Mon Sep 17 00:00:00 2001 From: tbohne Date: Wed, 17 Jan 2024 17:30:37 +0100 Subject: [PATCH 3/3] set default value for split_plot --- nbs/003_data.validation.ipynb | 100 +++++++++++++++++----------------- tsai/data/validation.py | 4 +- 2 files changed, 52 insertions(+), 52 deletions(-) diff --git a/nbs/003_data.validation.ipynb b/nbs/003_data.validation.ipynb index 727372ab9..2ae03f4cf 100644 --- a/nbs/003_data.validation.ipynb +++ b/nbs/003_data.validation.ipynb @@ -136,9 +136,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "imbalanced: [0 1 2 3 4] [26 60 27 73 54]\n", - "balanced (oversample): [0 1 2 3 4] [73 73 73 73 73]\n", - "balanced (undersample): [0 1 2 3 4] [26 26 26 26 26]\n" + "imbalanced: [0 1 2 3 4] [59 13 17 13 53]\n", + "balanced (oversample): [0 1 2 3 4] [59 59 59 59 59]\n", + "balanced (undersample): [0 1 2 3 4] [13 13 13 13 13]\n" ] } ], @@ -345,7 +345,7 @@ "outputs": [], "source": [ "#|export\n", - "def plot_splits(splits, contains_test_data):\n", + "def plot_splits(splits, contains_test_data=True):\n", " _max = 0\n", " _splits = 0\n", " for i, split in enumerate(splits):\n", @@ -470,7 +470,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -555,8 +555,8 @@ { "data": { "text/plain": [ - "((#256) [696,499,598,672,101,325,774,752,612,570...],\n", - " (#256) [696,499,598,672,101,325,774,752,612,570...])" + "((#256) [738,252,935,927,435,418,489,22,273,363...],\n", + " (#256) [738,252,935,927,435,418,489,22,273,363...])" ] }, "execution_count": 15, @@ -688,7 +688,7 @@ }, { "data": { - "image/png": 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" ] @@ -1064,7 +1064,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1128,7 +1128,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1152,7 +1152,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1176,7 +1176,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1212,7 +1212,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1285,7 +1285,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1298,9 +1298,9 @@ { "data": { "text/plain": [ - "((#60) [28,4,9,41,88,92,27,52,89,10...],\n", - " (#20) [42,90,8,71,55,75,77,69,44,11...],\n", - " (#20) [98,25,39,57,22,64,24,37,17,74...])" + "((#60) [34,59,17,8,55,21,75,2,22,61...],\n", + " (#20) [80,79,73,67,66,81,97,92,37,26...],\n", + " (#20) [36,31,5,89,56,72,28,47,54,65...])" ] }, "execution_count": 31, @@ -1359,7 +1359,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1383,7 +1383,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1396,9 +1396,9 @@ { "data": { "text/plain": [ - "((#12) [57,93,77,27,28,23,92,60,1,63...],\n", - " (#21) [64,59,38,58,2,73,80,46,24,89...],\n", - " (#17) [18,70,88,26,22,10,47,94,83,13...])" + "((#12) [23,69,78,72,58,29,6,61,25,94...],\n", + " (#21) [40,2,32,77,81,28,24,30,15,65...],\n", + " (#17) [62,99,35,4,82,20,48,44,42,97...])" ] }, "execution_count": 34, @@ -2536,13 +2536,13 @@ " test_split = L(np.arange(border1s[2], border2s[2] - fcst_horizon - fcst_history + 1).tolist()) \n", " splits = train_split, valid_split, test_split\n", " if show_plot:\n", - " plot_splits(splits)\n", + " plot_splits(splits, test_size > 0)\n", " return splits" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -2561,9 +2561,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "003_data.validation.ipynb saved at 2024-01-17 15:56:10\n", + "003_data.validation.ipynb saved at 2024-01-17 17:26:53\n", "Correct notebook to script conversion! 😃\n", - "Wednesday 17/01/24 15:56:13 CET\n" + "Wednesday 17/01/24 17:26:56 CET\n" ] }, { diff --git a/tsai/data/validation.py b/tsai/data/validation.py index b31117750..e0bfc3370 100644 --- a/tsai/data/validation.py +++ b/tsai/data/validation.py @@ -169,7 +169,7 @@ def _inner(o, **kwargs): return _inner # %% ../../nbs/003_data.validation.ipynb 13 -def plot_splits(splits, contains_test_data): +def plot_splits(splits, contains_test_data=True): _max = 0 _splits = 0 for i, split in enumerate(splits): @@ -670,5 +670,5 @@ def get_long_term_forecasting_splits( test_split = L(np.arange(border1s[2], border2s[2] - fcst_horizon - fcst_history + 1).tolist()) splits = train_split, valid_split, test_split if show_plot: - plot_splits(splits) + plot_splits(splits, test_size > 0) return splits