diff --git a/.gitignore b/.gitignore index cea410c7..1be68af1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# VSCode config +.vscode + # JupyterLab temp files .virtual_documents diff --git a/config/dev_reqs.txt b/config/dev_reqs.txt index b1ff8eaa..500793df 100644 --- a/config/dev_reqs.txt +++ b/config/dev_reqs.txt @@ -7,10 +7,11 @@ pytest pyyaml transformers>=3.0.0 # SpaCy models aren't stable across point releases -spacy==3.0.5 +spacy==3.6.0 ipywidgets ibm-watson twine +hypothesis # Documentation-related requirements have moved to non_36_reqs.txt. #sphinx diff --git a/config/non_36_reqs.txt b/config/non_36_reqs.txt index f6877e06..98e53e16 100644 --- a/config/non_36_reqs.txt +++ b/config/non_36_reqs.txt @@ -4,8 +4,14 @@ # This list will probably grow as libraries drop support. nltk -ray[default] -feather +ray[default] >= 2.0 + +# *** HACK ALERT *** +# Feather depends on Numpy being exactly 1.20.2, which breaks Pandas. +# So we don't include a dependency on it and hope for the best. +#feather +# *** END HACK *** + sphinx sphinxcontrib-apidoc diff --git a/notebooks/.gitignore b/notebooks/.gitignore new file mode 100644 index 00000000..217a2208 --- /dev/null +++ b/notebooks/.gitignore @@ -0,0 +1,2 @@ +CoNLL_u_test_inputs + diff --git a/notebooks/Analyze_Model_Outputs.ipynb b/notebooks/Analyze_Model_Outputs.ipynb index aaa1513f..102efeac 100644 --- a/notebooks/Analyze_Model_Outputs.ipynb +++ b/notebooks/Analyze_Model_Outputs.ipynb @@ -30,7 +30,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import os\n", @@ -72,7 +74,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -323,7 +327,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -504,7 +510,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -666,7 +674,9 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -4584,12 +4594,12 @@ } ], "source": [ - "from IPython.core.display import display, HTML\n", - "display(HTML(\"
{doc_text}\"))" ] }, @@ -376,9 +376,9 @@ " 'lemma': 'throughout'},\n", " {'text': 'Britain', 'part_of_speech': 'PROPN', 'location': [64, 71]},\n", " {'text': 'searching',\n", - " 'part_of_speech': 'VERB',\n", + " 'part_of_speech': 'NOUN',\n", " 'location': [72, 81],\n", - " 'lemma': 'search'},\n", + " 'lemma': 'searching'},\n", " {'text': 'for',\n", " 'part_of_speech': 'ADP',\n", " 'location': [82, 85],\n", @@ -412,13 +412,13 @@ " 'location': [113, 116],\n", " 'lemma': 'the'},\n", " {'text': 'Round',\n", - " 'part_of_speech': 'PROPN',\n", + " 'part_of_speech': 'ADJ',\n", " 'location': [117, 122],\n", - " 'lemma': 'Round'},\n", + " 'lemma': 'round'},\n", " {'text': 'Table',\n", - " 'part_of_speech': 'PROPN',\n", + " 'part_of_speech': 'NOUN',\n", " 'location': [123, 128],\n", - " 'lemma': 'Table'},\n", + " 'lemma': 'table'},\n", " {'text': '.', 'part_of_speech': 'PUNCT', 'location': [128, 129]},\n", " {'text': 'Along',\n", " 'part_of_speech': 'ADP',\n", @@ -492,10 +492,7 @@ " 'part_of_speech': 'DET',\n", " 'location': [236, 239],\n", " 'lemma': 'the'},\n", - " {'text': 'Not',\n", - " 'part_of_speech': 'ADV',\n", - " 'location': [240, 243],\n", - " 'lemma': 'not'},\n", + " {'text': 'Not', 'part_of_speech': 'PROPN', 'location': [240, 243]},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [243, 244]},\n", " {'text': 'Quite', 'part_of_speech': 'PROPN', 'location': [244, 249]},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [249, 250]},\n", @@ -505,9 +502,9 @@ " 'lemma': 'so'},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [252, 253]},\n", " {'text': 'Brave',\n", - " 'part_of_speech': 'ADJ',\n", + " 'part_of_speech': 'PROPN',\n", " 'location': [253, 258],\n", - " 'lemma': 'brave'},\n", + " 'lemma': 'Brave'},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [258, 259]},\n", " {'text': 'as',\n", " 'part_of_speech': 'ADP',\n", @@ -698,10 +695,7 @@ " 'part_of_speech': 'VERB',\n", " 'location': [521, 525],\n", " 'lemma': 'turn'},\n", - " {'text': 'away',\n", - " 'part_of_speech': 'ADV',\n", - " 'location': [526, 530],\n", - " 'lemma': 'away'},\n", + " {'text': 'away', 'part_of_speech': 'ADP', 'location': [526, 530]},\n", " {'text': ',', 'part_of_speech': 'PUNCT', 'location': [530, 531]},\n", " {'text': 'God',\n", " 'part_of_speech': 'PROPN',\n", @@ -4726,7 +4720,7 @@ "
Pipeline(steps=[('mlogreg',\n", + " LogisticRegression(C=0.1, max_iter=10000,\n", + " multi_class='multinomial', verbose=1))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('mlogreg',\n", + " LogisticRegression(C=0.1, max_iter=10000,\n", + " multi_class='multinomial', verbose=1))])
LogisticRegression(C=0.1, max_iter=10000, multi_class='multinomial', verbose=1)
25151 rows × 5 columns
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\n", "" ], "text/plain": [ @@ -799,13 +796,13 @@ "3 [12, 17): 'comes' come VERB\n", + " | fold | \n", + "doc_num | \n", + "token_id | \n", + "span | \n", + "input_id | \n", + "token_type_id | \n", + "attention_mask | \n", + "special_tokens_mask | \n", + "postag | \n", + "raw_span | \n", + "raw_span_id | \n", + "postag_id | \n", + "embedding | \n", + "text | \n", + "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", + "test | \n", + "0 | \n", + "0 | \n", + "[0, 0): '' | \n", + "101 | \n", + "0 | \n", + "1 | \n", + "True | \n", + "X | \n", + "NaN | \n", + "NaN | \n", + "14 | \n", + "[ -0.37686658, -0.14841351, 0.739800... | \n", + "\n", + " |
1 | \n", + "test | \n", + "0 | \n", + "1 | \n", + "[0, 4): 'What' | \n", + "1327 | \n", + "0 | \n", + "1 | \n", + "False | \n", + "PRON | \n", + "[0, 4): 'What' | \n", + "0.0 | \n", + "11 | \n", + "[ -0.23266977, -0.40546313, 0.6171927... | \n", + "What | \n", + "
2 | \n", + "test | \n", + "0 | \n", + "2 | \n", + "[5, 7): 'if' | \n", + "1191 | \n", + "0 | \n", + "1 | \n", + "False | \n", + "SCONJ | \n", + "[5, 7): 'if' | \n", + "1.0 | \n", + "13 | \n", + "[ -0.81568515, -0.047825783, 0.0814849... | \n", + "if | \n", + "
3 | \n", + "test | \n", + "0 | \n", + "3 | \n", + "[8, 14): 'Google' | \n", + "7986 | \n", + "0 | \n", + "1 | \n", + "False | \n", + "PROPN | \n", + "[8, 14): 'Google' | \n", + "2.0 | \n", + "2 | \n", + "[ 0.7896778, -0.85118735, -0.4881255... | \n", + "|
4 | \n", + "test | \n", + "0 | \n", + "4 | \n", + "[15, 17): 'Mo' | \n", + "12556 | \n", + "0 | \n", + "1 | \n", + "False | \n", + "VERB | \n", + "[15, 22): 'Morphed' | \n", + "3.0 | \n", + "3 | \n", + "[ -0.25935066, 0.57107216, -0.0910669... | \n", + "Mo | \n", + "
... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
307907 | \n", + "train | \n", + "539 | \n", + "756 | \n", + "[3152, 3154): 'my' | \n", + "1139 | \n", + "0 | \n", + "1 | \n", + "False | \n", + "PRON | \n", + "[3152, 3154): 'my' | \n", + "690.0 | \n", + "11 | \n", + "[ -0.06984619, -0.4646066, 0.854770... | \n", + "my | \n", + "
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307909 | \n", + "train | \n", + "539 | \n", + "758 | \n", + "[3158, 3159): ')' | \n", + "114 | \n", + "0 | \n", + "1 | \n", + "False | \n", + "PUNCT | \n", + "[3158, 3159): ')' | \n", + "692.0 | \n", + "5 | \n", + "[ -0.09065091, -0.29592815, 0.5970235... | \n", + ") | \n", + "
307910 | \n", + "train | \n", + "539 | \n", + "759 | \n", + "[3159, 3160): '.' | \n", + "119 | \n", + "0 | \n", + "1 | \n", + "False | \n", + "PUNCT | \n", + "[3159, 3160): '.' | \n", + "693.0 | \n", + "5 | \n", + "[ 0.03102289, -0.27608734, 0.782190... | \n", + ". | \n", + "
307911 | \n", + "train | \n", + "539 | \n", + "760 | \n", + "[0, 0): '' | \n", + "102 | \n", + "0 | \n", + "1 | \n", + "True | \n", + "X | \n", + "NaN | \n", + "NaN | \n", + "14 | \n", + "[ -0.50887, -0.22885998, 0.54494... | \n", + "\n", + " |
307912 rows × 14 columns
\n", + "243168 rows × 14 columns
\n", + "243181 rows × 14 columns
\n", "" ], "text/plain": [ " fold doc_num token_id span input_id \\\n", - "64729 train 0 0 [0, 0): '' 101 \n", - "64730 train 0 1 [0, 2): 'Al' 2586 \n", - "64731 train 0 2 [2, 3): '-' 118 \n", - "64732 train 0 3 [4, 5): 'Z' 163 \n", - "64733 train 0 4 [5, 9): 'aman' 19853 \n", + "64731 train 0 0 [0, 0): '' 101 \n", + "64732 train 0 1 [0, 2): 'Al' 2586 \n", + "64733 train 0 2 [2, 3): '-' 118 \n", + "64734 train 0 3 [4, 5): 'Z' 163 \n", + "64735 train 0 4 [5, 9): 'aman' 19853 \n", "... ... ... ... ... ... \n", - "307892 train 539 756 [3152, 3154): 'my' 1139 \n", - "307893 train 539 757 [3155, 3158): 'car' 1610 \n", - "307894 train 539 758 [3158, 3159): ')' 114 \n", - "307895 train 539 759 [3159, 3160): '.' 119 \n", - "307896 train 539 760 [0, 0): '' 102 \n", + "307907 train 539 756 [3152, 3154): 'my' 1139 \n", + "307908 train 539 757 [3155, 3158): 'car' 1610 \n", + "307909 train 539 758 [3158, 3159): ')' 114 \n", + "307910 train 539 759 [3159, 3160): '.' 119 \n", + "307911 train 539 760 [0, 0): '' 102 \n", "\n", " token_type_id attention_mask special_tokens_mask postag \\\n", - "64729 0 1 True X \n", - "64730 0 1 False PROPN \n", - "64731 0 1 False PUNCT \n", + "64731 0 1 True X \n", "64732 0 1 False PROPN \n", - "64733 0 1 False PROPN \n", + "64733 0 1 False PUNCT \n", + "64734 0 1 False PROPN \n", + "64735 0 1 False PROPN \n", "... ... ... ... ... \n", - "307892 0 1 False PRON \n", - "307893 0 1 False NOUN \n", - "307894 0 1 False PUNCT \n", - "307895 0 1 False PUNCT \n", - "307896 0 1 True X \n", + "307907 0 1 False PRON \n", + "307908 0 1 False NOUN \n", + "307909 0 1 False PUNCT \n", + "307910 0 1 False PUNCT \n", + "307911 0 1 True X \n", "\n", " raw_span raw_span_id postag_id \\\n", - "64729 NaN NaN 14 \n", - "64730 [0, 2): 'Al' 0.0 2 \n", - "64731 [2, 3): '-' 1.0 5 \n", - "64732 [4, 9): 'Zaman' 2.0 2 \n", - "64733 [4, 9): 'Zaman' 2.0 2 \n", + "64731 NaN NaN 14 \n", + "64732 [0, 2): 'Al' 0.0 2 \n", + "64733 [2, 3): '-' 1.0 5 \n", + "64734 [4, 9): 'Zaman' 2.0 2 \n", + "64735 [4, 9): 'Zaman' 2.0 2 \n", "... ... ... ... \n", - "307892 [3152, 3154): 'my' 690.0 11 \n", - "307893 [3155, 3158): 'car' 691.0 4 \n", - "307894 [3158, 3159): ')' 692.0 5 \n", - "307895 [3159, 3160): '.' 693.0 5 \n", - "307896 NaN NaN 14 \n", + "307907 [3152, 3154): 'my' 690.0 11 \n", + "307908 [3155, 3158): 'car' 691.0 4 \n", + "307909 [3158, 3159): ')' 692.0 5 \n", + "307910 [3159, 3160): '.' 693.0 5 \n", + "307911 NaN NaN 14 \n", "\n", " embedding text \n", - "64729 [ -0.41927838, -0.22575253, 0.6648760... \n", - "64730 [ -0.36961424, -1.0804733, -0.283367... Al \n", - "64731 [ -0.9178737, -0.94624436, -0.808995... - \n", - "64732 [ -0.90530086, -0.97086835, -1.440879... Z \n", - "64733 [ -1.1586123, -1.149766, -1.194975... aman \n", + "64731 [ -0.41927913, -0.22575217, 0.6648752... \n", + "64732 [ -0.36961484, -1.0804743, -0.2833683... Al \n", + "64733 [ -0.9178743, -0.9462442, -0.808995... - \n", + "64734 [ -0.90530103, -0.97086823, -1.440878... Z \n", + "64735 [ -1.158612, -1.1497651, -1.194976... aman \n", "... ... ... \n", - "307892 [ -0.06984596, -0.4646067, 0.8547705... my \n", - "307893 [ 0.14624132, -0.46386197, 0.596684... car \n", - "307894 [ -0.090651065, -0.29592788, 0.597023... ) \n", - "307895 [ 0.031023545, -0.27608734, 0.782190... . \n", - "307896 [ -0.5088702, -0.22885968, 0.544944... \n", + "307907 [ -0.06984619, -0.4646066, 0.854770... my \n", + "307908 [ 0.14624149, -0.46386155, 0.596684... car \n", + "307909 [ -0.09065091, -0.29592815, 0.5970235... ) \n", + "307910 [ 0.03102289, -0.27608734, 0.782190... . \n", + "307911 [ -0.50887, -0.22885998, 0.54494... \n", "\n", - "[243168 rows x 14 columns]" + "[243181 rows x 14 columns]" ] }, "execution_count": 14, @@ -2010,29 +2299,120 @@ "id": "034a61f3-7fe8-4b02-b649-cea67e14ceb3", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 13073 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 6.88984D+05 |proj g|= 6.62729D+04\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "/Users/freiss/opt/miniconda3/envs/pd/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + " This problem is unconstrained.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "At iterate 50 f= 2.44541D+05 |proj g|= 3.93970D+03\n", + "\n", + "At iterate 100 f= 1.63368D+05 |proj g|= 1.71818D+03\n", + "\n", + "At iterate 150 f= 1.32218D+05 |proj g|= 1.03361D+03\n", + "\n", + "At iterate 200 f= 1.18130D+05 |proj g|= 7.32021D+02\n", + "\n", + "At iterate 250 f= 1.09684D+05 |proj g|= 1.23366D+03\n", + "\n", + "At iterate 300 f= 1.05398D+05 |proj g|= 6.35734D+02\n", + "\n", + "At iterate 350 f= 1.02851D+05 |proj g|= 2.76671D+02\n", + "\n", + "At iterate 400 f= 1.01228D+05 |proj g|= 5.09281D+02\n", + "\n", + "At iterate 450 f= 1.00038D+05 |proj g|= 3.14557D+02\n", + "\n", + "At iterate 500 f= 9.92494D+04 |proj g|= 1.68499D+02\n", + "\n", + "At iterate 550 f= 9.88417D+04 |proj g|= 4.91916D+02\n", + "\n", + "At iterate 600 f= 9.85123D+04 |proj g|= 2.02593D+02\n", + "\n", + "At iterate 650 f= 9.82550D+04 |proj g|= 1.28953D+02\n", + "\n", + "At iterate 700 f= 9.81148D+04 |proj g|= 1.09533D+02\n", + "\n", + "At iterate 750 f= 9.80368D+04 |proj g|= 8.88012D+01\n", + "\n", + "At iterate 800 f= 9.79714D+04 |proj g|= 7.48262D+01\n", + "\n", + "At iterate 850 f= 9.79321D+04 |proj g|= 1.00950D+02\n", + "\n", + "At iterate 900 f= 9.79023D+04 |proj g|= 2.59398D+01\n", + "\n", + "At iterate 950 f= 9.78679D+04 |proj g|= 3.74091D+01\n", + "\n", + "At iterate 1000 f= 9.78449D+04 |proj g|= 3.24331D+01\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + "13073 1000 1065 1 0 0 3.243D+01 9.784D+04\n", + " F = 97844.884007299028 \n", + "\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/freiss/opt/miniconda3/envs/pd/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 16.6min remaining: 0.0s\n", - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 16.6min finished\n" + " n_iter_i = _check_optimize_result(\n" ] }, { "data": { + "text/html": [ + "Pipeline(steps=[('mlogreg',\n", + " LogisticRegression(C=0.1, max_iter=1000,\n", + " multi_class='multinomial', verbose=1))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('mlogreg',\n", + " LogisticRegression(C=0.1, max_iter=1000,\n", + " multi_class='multinomial', verbose=1))])
LogisticRegression(C=0.1, max_iter=1000, multi_class='multinomial', verbose=1)
\n", - " | emotion.anger | \n", - "emotion.disgust | \n", - "emotion.fear | \n", - "emotion.joy | \n", - "emotion.sadness | \n", - "relevance | \n", - "sentiment.score | \n", - "Rating\r", + " | emotion.sadness | \n", + "emotion.joy | \n", + "emotion.fear | \n", + "emotion.disgust | \n", + "emotion.anger | \n", + "sentiment.score | \n", + "Rating\r", " | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
emotion.anger | \n", - "1.00 | \n", - "0.46 | \n", - "0.43 | \n", - "-0.52 | \n", - "0.46 | \n", - "-0.13 | \n", - "-0.54 | \n", - "-0.39 | \n", - "|||||||
emotion.disgust | \n", - "0.46 | \n", - "1.00 | \n", - "0.30 | \n", - "-0.45 | \n", - "0.38 | \n", - "-0.16 | \n", - "-0.43 | \n", - "-0.32 | \n", - "|||||||
emotion.fear | \n", - "0.43 | \n", - "0.30 | \n", - "1.00 | \n", - "-0.52 | \n", - "0.46 | \n", - "-0.13 | \n", - "-0.48 | \n", - "-0.31 | \n", - "|||||||
emotion.joy | \n", - "-0.52 | \n", - "-0.45 | \n", - "-0.52 | \n", - "1.00 | \n", - "-0.64 | \n", - "0.24 | \n", - "0.71 | \n", - "0.46 | \n", - "|||||||
emotion.sadness | \n", - "0.46 | \n", - "0.38 | \n", - "0.46 | \n", - "-0.64 | \n", - "1.00 | \n", - "-0.16 | \n", - "-0.66 | \n", - "-0.47 | \n", - "|||||||
relevance | \n", - "-0.13 | \n", - "-0.16 | \n", - "-0.13 | \n", - "0.24 | \n", - "-0.16 | \n", - "1.00 | \n", - "0.17 | \n", - "0.10 | \n", - "|||||||
sentiment.score | \n", - "-0.54 | \n", - "-0.43 | \n", - "-0.48 | \n", - "0.71 | \n", - "-0.66 | \n", - "0.17 | \n", - "1.00 | \n", - "0.61 | \n", - "|||||||
Rating\r", + " | emotion.sadness | \n", + "1.000000 | \n", + "-0.635496 | \n", + "0.099018 | \n", + "0.062068 | \n", + "0.158305 | \n", + "-0.519823 | \n", + "-0.353612 | \n", + "|||||||
emotion.joy | \n", + "-0.635496 | \n", + "1.000000 | \n", + "-0.391679 | \n", + "-0.226616 | \n", + "-0.484740 | \n", + "0.761211 | \n", + "0.518204 | \n", + "||||||||
emotion.fear | \n", + "0.099018 | \n", + "-0.391679 | \n", + "1.000000 | \n", + "0.077824 | \n", + "0.149845 | \n", + "-0.321152 | \n", + "-0.187657 | \n", + "||||||||
emotion.disgust | \n", + "0.062068 | \n", + "-0.226616 | \n", + "0.077824 | \n", + "1.000000 | \n", + "0.136611 | \n", + "-0.213541 | \n", + "-0.155754 | \n", + "||||||||
emotion.anger | \n", + "0.158305 | \n", + "-0.484740 | \n", + "0.149845 | \n", + "0.136611 | \n", + "1.000000 | \n", + "-0.440811 | \n", + "-0.352083 | \n", + "||||||||
sentiment.score | \n", + "-0.519823 | \n", + "0.761211 | \n", + "-0.321152 | \n", + "-0.213541 | \n", + "-0.440811 | \n", + "1.000000 | \n", + "0.620320 | \n", + "||||||||
Rating\r", " | \n", - "-0.39 | \n", - "-0.32 | \n", - "-0.31 | \n", - "0.46 | \n", - "-0.47 | \n", - "0.10 | \n", - "0.61 | \n", - "1.00 | \n", + "-0.353612 | \n", + "0.518204 | \n", + "-0.187657 | \n", + "-0.155754 | \n", + "-0.352083 | \n", + "0.620320 | \n", + "1.000000 | \n", "
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
1529 rows × 2 columns
\n", + "1532 rows × 2 columns
\n", "" ], "text/plain": [ " Predicted Rating Actual Rating\n", - "0 5.235334 2.375000\n", - "1 4.735796 4.875000\n", - "2 4.567478 4.750000\n", - "3 4.404560 5.000000\n", - "4 5.188259 5.000000\n", + "0 4.300783 4.875\n", + "1 3.895731 4.625\n", + "2 4.342326 3.375\n", + "3 4.705346 4.875\n", + "4 3.256459 5.000\n", "... ... ...\n", - "1524 4.547005 5.000000\n", - "1525 4.129847 5.000000\n", - "1526 4.019317 1.886364\n", - "1527 4.591441 3.500000\n", - "1528 4.195468 4.125000\n", + "1527 4.623339 3.125\n", + "1528 3.747142 5.000\n", + "1529 3.874102 4.500\n", + "1530 3.468226 3.875\n", + "1531 4.406095 5.000\n", "\n", - "[1529 rows x 2 columns]" + "[1532 rows x 2 columns]" ] }, "execution_count": 39, @@ -4541,19 +4091,17 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "RandomForestRegressor(random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor(random_state=0)
ARMONK, N.Y., Jan. 4, 2021 /PRNewswire/ -- Many corporate IT leaders say their organizations are not prepared for the future IT needs of the business and nearly all are moving to advance their transition to cloud infrastructure, according to a new IBM (NYSE: IBM) survey of leaders at mid-sized and large companies in the United States and United Kingdom.
Of the 380 CIOs and CTOs who participated in the survey, 60% say their company's IT modernization program is not yet ready for the future, according to the recently completed The State of IT Transformation Study conducted by the Managed Infrastructure Services unit of IBM's Global Technology Services division. Nearly a quarter of CIOs and CTOs (24%) surveyed say their company is just starting its IT modernization journey or has yet to begin modernizing, with about a third surveyed saying they are still in the [...]" + "
IBM Newsroom
ARMONK, N.Y., Jan. 4, 2021 /PRNewswire/ -- Many corporate IT leaders say their organizations are not prepared for the future IT needs of the business and nearly all are moving to advance their transition to cloud infrastructure, according to a new IBM (NYSE: IBM) survey of leaders at mid-sized and large companies in the United States and United Kingdom.
Of the 380 CIOs and CTOs who participated in the survey, 60% say their company's IT modernization program is not yet ready for the future, according to the recently completed The State of IT Transformation Study conducted by the Managed Infrastructure Services unit of IBM's Global Technology Services division. Nearly a quarter of CIOs and CTOs (24%) surveyed say their company is just starting its IT modernization journey or has yet to begin modernizing, with about a third surveyed saying they are still in the midst of [...]"
],
"text/plain": [
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- " \n",
" 38 \n",
+ " 26 \n",
" [1213, 1229): 'Christoph Herman' \n",
- " 0.944350 \n",
+ " 0.992954 \n",
" \n",
- " \n",
- " 41 \n",
+ " 31 \n",
" [2227, 2242): 'Stephen Leonard' \n",
- " 0.989177 \n",
- " \n",
- " \n",
" \n",
"\n",
@@ -211,9 +199,8 @@
],
"text/plain": [
" person confidence\n",
- "38 [1213, 1229): 'Christoph Herman' 0.944350\n",
- "41 [2227, 2242): 'Stephen Leonard' 0.989177\n",
- "48 [3574, 3585): 'Sam Ponedal' 0.894298"
+ "26 [1213, 1229): 'Christoph Herman' 0.992954\n",
+ "31 [2227, 2242): 'Stephen Leonard' 0.995480"
]
},
"execution_count": 6,
@@ -271,9 +258,9 @@
" \n",
" 48 \n",
- " [3574, 3585): 'Sam Ponedal' \n",
- " 0.894298 \n",
+ " 0.995480 \n",
" \n",
" \n",
" \n",
"\n",
@@ -282,11 +269,11 @@
"text/plain": [
" subject verb \\\n",
"0 [1213, 1281): 'Christoph Herman, SVP and Head ... said \n",
- "1 [2227, 2519): 'Stephen Leonard, General Manage... said \n",
+ "1 [2227, 2282): 'Stephen Leonard, General Manage... said \n",
"\n",
" object \n",
"0 [937, 1205): 'SAP HANA Enterprise Cloud on IBM... \n",
- "1 [2028, 2219): 'In June, IBM announced the avai... "
+ "1 [2352, 2519): ', we're giving our clients more... "
]
},
"execution_count": 7,
@@ -327,17 +314,17 @@
"[{'subject': {'text': 'Christoph Herman, SVP and Head of SAP HANA Enterprise Cloud Delivery',\n",
" 'begin': 1213,\n",
" 'end': 1281},\n",
- " 'sentence': ' \"SAP HANA Enterprise Cloud on IBM Power Systems will help clients unlock the full value of SAP HANA in the cloud, with the possibility of enhancing the scalability and availability of mission critical SAP applications while moving workloads to SAP HANA and lowering TCO,\" said Christoph Herman, SVP and Head of SAP HANA Enterprise Cloud Delivery.',\n",
+ " 'sentence': '\"SAP HANA Enterprise Cloud on IBM Power Systems will help clients unlock the full value of SAP HANA in the cloud, with the possibility of enhancing the scalability and availability of mission critical SAP applications while moving workloads to SAP HANA and lowering TCO,\" said Christoph Herman, SVP and Head of SAP HANA Enterprise Cloud Delivery.',\n",
" 'object': {'text': 'SAP HANA Enterprise Cloud on IBM Power Systems will help clients unlock the full value of SAP HANA in the cloud, with the possibility of enhancing the scalability and availability of mission critical SAP applications while moving workloads to SAP HANA and lowering TCO'},\n",
- " 'action': {'verb': {'text': 'say', 'tense': 'past'},\n",
+ " 'action': {'verb': {'text': 'say', 'tense': 'future'},\n",
" 'text': 'said',\n",
" 'normalized': 'say'}},\n",
- " {'subject': {'text': 'Stephen Leonard, General Manager, IBM Cognitive Systems, \"With the addition of IBM Power Systems in SAP HANA Enterprise Cloud, we\\'re giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation',\n",
+ " {'subject': {'text': 'Stephen Leonard, General Manager, IBM Cognitive Systems',\n",
" 'begin': 2227,\n",
- " 'end': 2519},\n",
- " 'sentence': ' \"In June, IBM announced the availability of POWER9 in the IBM Cloud, taking the first step toward our goal of bringing IBM Cognitive Systems technology to our clients, no matter where they are,\" said Stephen Leonard, General Manager, IBM Cognitive Systems, \"With the addition of IBM Power Systems in SAP HANA Enterprise Cloud, we\\'re giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation.\"',\n",
- " 'object': {'text': 'In June, IBM announced the availability of POWER9 in the IBM Cloud, taking the first step toward our goal of bringing IBM Cognitive Systems technology to our clients, no matter where they are'},\n",
- " 'action': {'verb': {'text': 'say', 'tense': 'past'},\n",
+ " 'end': 2282},\n",
+ " 'sentence': '\"In June, IBM announced the availability of POWER9 in the IBM Cloud, taking the first step toward our goal of bringing IBM Cognitive Systems technology to our clients, no matter where they are,\" said Stephen Leonard, General Manager, IBM Cognitive Systems, \"With the addition of IBM Power Systems in SAP HANA Enterprise Cloud, we\\'re giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation.\"',\n",
+ " 'object': {'text': \", we're giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation\"},\n",
+ " 'action': {'verb': {'text': 'say', 'tense': 'present'},\n",
" 'text': 'said',\n",
" 'normalized': 'say'}}]"
]
@@ -349,7 +336,6 @@
],
"source": [
"# Code for slides: Run the Watson NLU semantic_roles model\n",
- "\n",
"semantic_roles_results = (\n",
" natural_language_understanding\n",
" .analyze(url=doc_url, features=nlu.Features(\n",
@@ -360,7 +346,7 @@
"for s in someone_said_something:\n",
" s[\"subject\"][\"begin\"] = doc_text.find(s[\"subject\"][\"text\"])\n",
" s[\"subject\"][\"end\"] = s[\"subject\"][\"begin\"] + len(s[\"subject\"][\"text\"])\n",
- " \n",
+ "\n",
"\n",
"someone_said_something_json = someone_said_something\n",
"someone_said_something_json"
@@ -371,11 +357,19 @@
"execution_count": 9,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
+ "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+ ]
+ },
{
"data": {
"text/plain": [
"[{'entity_group': 'PER',\n",
- " 'score': 0.9996308088302612,\n",
+ " 'score': 0.99963087,\n",
" 'word': 'Christoph Herman',\n",
" 'start': 1213,\n",
" 'end': 1229}]"
@@ -464,6 +458,9 @@
"source": [
"# Write out all the data we've generated\n",
"output_dir = \"./scipy_demo_data\"\n",
+ "if os.path.exists(output_dir):\n",
+ " shutil.rmtree(output_dir)\n",
+ "os.mkdir(output_dir)\n",
"\n",
"###################\n",
"# Inputs to Part 1\n",
@@ -472,15 +469,16 @@
"for m in person_mentions:\n",
" m[\"start\"] = int(m[\"start\"])\n",
" m[\"end\"] = int(m[\"end\"])\n",
+ " m[\"score\"] = float(m[\"score\"])\n",
"with open(f\"{output_dir}/person_mentions.json\", \"w\") as f:\n",
" json.dump(person_mentions, f)\n",
- " \n",
+ "\n",
"with open(f\"{output_dir}/person_mentions_watson.json\", \"w\") as f:\n",
" json.dump(person_mentions_watson_json, f)\n",
- " \n",
+ "\n",
"with open(f\"{output_dir}/someone_said_something.json\", \"w\") as f:\n",
" json.dump(someone_said_something_json, f)\n",
- " \n",
+ "\n",
"###################\n",
"# Inputs to Part 2\n",
"\n",
@@ -499,7 +497,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -513,7 +511,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.10"
+ "version": "3.8.17"
}
},
"nbformat": 4,
diff --git a/tutorials/market/ibm_press_releases.txt b/tutorials/market/ibm_press_releases.txt
index 453381d6..8383375d 100644
--- a/tutorials/market/ibm_press_releases.txt
+++ b/tutorials/market/ibm_press_releases.txt
@@ -47,7 +47,7 @@ https://newsroom.ibm.com/2020-05-21-we-trade-Digital-Trade-Finance-Network-stren
https://newsroom.ibm.com/2020-07-30-IBM-and-Influential-Launch-AI-enabled-Social-Targeting-Solution-To-Help-Brands-Identify-Suitable-Influencers
https://newsroom.ibm.com/2020-06-03-IBM-services-and-Aegon-sign-contract-of-portfolio-administration-for-800-000-individual-life-insurance-contracts
https://newsroom.ibm.com/2020-06-03-IBM-and-Persistent-Systems-to-Accelerate-IBM-Cloud-Pak-Deployment-and-Core-IT-Modernization-for-Enterprises
-https://newsroom.ibm.com/2020-06-04-Spains-CaixaBank-Teams-with-IBM-Services-to-Accelerate-Cloud-Transformation-and-Innovation-in-the-Financial-Services-Industry
+#https://newsroom.ibm.com/2020-06-04-Spains-CaixaBank-Teams-with-IBM-Services-to-Accelerate-Cloud-Transformation-and-Innovation-in-the-Financial-Services-Industry
https://newsroom.ibm.com/2020-06-04-IBM-Services-Collaborates-with-Lotte-Card-to-Adopt-A-Hybrid-Cloud-Strategy-To-Help-Transform-Core-Financial-Accounting-Systems
https://newsroom.ibm.com/2020-06-04-Kvar-y-Arctic-Using-IBM-Blockchain-to-Trace-Norwegian-Farmed-Salmon-to-North-American-Stores
https://newsroom.ibm.com/2020-06-04-Anaconda-and-IBM-Watson-Team-to-Simplify-Enterprise-Adoption-of-AI-Open-Source-Technologies
@@ -116,7 +116,7 @@ https://newsroom.ibm.com/2020-09-18-Bloomberg-Television-Intelligence-Squared-US
https://newsroom.ibm.com/2020-09-21-GEODIS-Uses-IBM-Sterling-Order-Management-to-Help-Retailers-Accelerate-Omnichannel-Customer-Experience-Capabilities-with-New-e-Commerce-Fulfillment
https://newsroom.ibm.com/2020-09-22-IBM-Brings-Risk-Analytics-to-Security-Decision-Making
https://newsroom.ibm.com/2020-09-23-IBM-Modernizes-Financial-Transaction-Solution-on-Red-Hat-OpenShift-to-Give-Banks-the-Flexibility-of-Hybrid-Cloud
-https://newsroom.ibm.com/2020-09-28-2020-Call-for-Code-Global-Challenge-Finalists-Selected-for-Innovative-Solutions-to-Take-on-COVID-19-and-Climate-Change
+#https://newsroom.ibm.com/2020-09-28-2020-Call-for-Code-Global-Challenge-Finalists-Selected-for-Innovative-Solutions-to-Take-on-COVID-19-and-Climate-Change
https://newsroom.ibm.com/2020-09-30-IBM-Study-Majority-of-Global-C-Suite-Executives-are-Rapidly-Accelerating-Digital-Transformation-due-to-COVID-19-Pandemic-but-People-and-Talent-are-Key-to-Future-Progress
https://newsroom.ibm.com/2020-09-30-BCI-in-Collaboration-with-IBM-Advances-Blockchain-based-Financial-Services-with-Electronic-Letter-of-Guarantee-for-Clients-in-Thailand
diff --git a/tutorials/market/market_intelligence.py b/tutorials/market/market_intelligence.py
index 5d1707ec..88f10373 100644
--- a/tutorials/market/market_intelligence.py
+++ b/tutorials/market/market_intelligence.py
@@ -31,9 +31,13 @@ def maybe_download_articles() -> pd.DataFrame:
lines = [l.strip() for l in f.readlines()]
article_urls = [l for l in lines if len(l) > 0 and l[0] != "#"]
- article_htmls = [
- download_article(url) for url in article_urls
- ]
+ article_htmls = []
+ for url in article_urls:
+ try:
+ article_htmls.append(download_article(url))
+ except urllib.error.HTTPError as e:
+ raise ValueError(f"Error downloading {url}") from e
+
to_write = pd.DataFrame({"url": article_urls,
"html": article_htmls})
to_write.to_feather(file_name)
1 \n",
- " [2227, 2519): 'Stephen Leonard, General Manage... \n",
+ " [2227, 2282): 'Stephen Leonard, General Manage... \n",
" said \n",
- " [2028, 2219): 'In June, IBM announced the avai... \n",
+ " [2352, 2519): ', we're giving our clients more... \n",
"