Create earthquaqeIdentification

earthquaqeIdentification

@@ -0,0 +1,142 @@
+import spacy
+import numpy as np
+
+# Load the spacy model with GloVe embeddings
+nlp = spacy.load("en_core_web_lg")
+
+# Define earthquake-related keywords
+earthquake_single_keywords = ['earthquake', 'seismic', 'tremor', 'quake', 'aftershock', 'seismology', 'tectonic', 'plate', 'seismometer', 'temblor', 'trembler', 'seism', 'shock', 'vibration', 'shake', 'groundswell', 'earthquakes', 'seismics', 'tremors', 'quakes', 'aftershocks', 'seismologies', 'tectonics', 'plates', 'seismometers', 'temblors', 'tremblers', 'seisms', 'shocks', 'vibrations', 'shakes', 'groundswells']
+
+# Compute embeddings for single-word keywords
+earthquake_single_embeddings = [nlp(keyword).vector for keyword in earthquake_single_keywords]
+
+# Define multi-word earthquake-related keywords
+earthquake_multi_keywords = ['seismic activity', 'earthquake risk', 'earthquake zone', 'seismic wave', 'earthquake damage', 'seismic shift', 'tectonic plate', 'fault line', 'seismic retrofitting', 'seismic hazard', 'aftershock sequence', 'earthquake drill', 'seismic reflection', 'plate tectonic', 'seismic tomography', 'seismic profiling', 'seismicity pattern', 'earthquake swarm', 'seismic gap', 'seismic inversion', 'seismic reflection', 'seismic scattering', 'seismic attenuation', 'seismic imaging', 'seismic map', 'seismic data', 'earthquake monitoring', 'earth shaking', 'seismic activities', 'earthquake risks', 'earthquake zones', 'seismic waves', 'earthquake damages', 'seismic shifts', 'tectonic plates', 'fault lines', 'seismic retrofittings', 'seismic hazards', 'aftershock sequences', 'earthquake drills', 'seismic reflections', 'plate tectonics', 'seismic tomographies', 'seismic profilings', 'seismicity patterns', 'earthquake swarms', 'seismic gaps', 'seismic inversions', 'seismic reflections', 'seismic scatterings', 'seismic attenuations', 'seismic imagings', 'seismic maps', 'earth shakings']
+
+# Compute embeddings for multi-word keywords
+earthquake_multi_embeddings = []
+for keyword in earthquake_multi_keywords:
+    keyword_embeddings = [nlp(w).vector for w in keyword.split()]
+    combined_emb = np.mean(keyword_embeddings, axis=0)  # Compute the average embedding for the multi-word token
+    earthquake_multi_embeddings.append(combined_emb)
+
+def straight_pattern_matching(ngram):
+  """
+  Function to compute a straightforward similarity between a word and the pre-defined references
+  """
+  if ngram in earthquake_single_keywords or ngram in earthquake_multi_keywords:
+    return ngram
+  else:
+    return False
+
+
+# Define a function to compute the semantic similarity between a word and a set of embeddings
+def compute_similarity_earthquake(word, embeddings, excluded_keywords):
+    """
+    Compute the semantic similarity for earthquaqe events
+    """
+
+    # Check if the word is in the excluded keywords list
+    if word in excluded_keywords:
+        return False
+
+    # Compute the GloVe embedding of the word
+    word_emb = nlp(word).vector
+
+    # Compute the cosine similarity between the word embedding and the keyword embeddings
+    similarity_scores = [np.dot(word_emb, emb) / (np.linalg.norm(word_emb) * np.linalg.norm(emb)) for emb in embeddings]
+
+    # Return if the maximum similarity score is above a certain threshold
+    if max(similarity_scores) > 0.7:
+      return word
+
+    else:
+      return False
+
+
+def identify_earthquake_event(input_sentence):
+    """
+    Compute the semantic similarity for earthquaqe events
+    """
+
+    try:
+
+        # Define excluded keywords to ignore (because cases like I want bars with magnituted 6 - were given as correct)
+        excluded_keywords = ['magnitude', 'richter', 'moment', 'scale', 'intensity', 'amplitude', 'energy', 'force',
+                             'power', 'seismicity', 'event',
+                             'magnitudes', 'richters', 'moments', 'scales', 'intensities', 'amplitudes', 'energies',
+                             'forces', 'powers', 'seismicities', 'events']
+
+        parsed_sentence = nlp(input_sentence)
+
+        # start with simple straight pattern matching of single keywords
+        for word in parsed_sentence:
+            if word.text not in excluded_keywords:
+                straight_matching_single = straight_pattern_matching(word.text)
+
+                if straight_matching_single:
+                    # return {'earthquaqe_event': [True, straight_matching_single]}
+                    return {"event": "earthquake"}
+
+        # Continue with embeddings single matching
+        earthquaqe_keywords_single = []
+
+        # Check for single-word earthquake-related keywords
+        earthquaqe_keywords_single = [
+            compute_similarity_earthquake(word.text.lower(), earthquake_single_embeddings, excluded_keywords) for word
+            in parsed_sentence]
+
+        single_keyword_flag = False
+
+        # check until you find one such reference and then break
+        for elem in earthquaqe_keywords_single:
+            if elem:
+                single_keyword_flag = True
+                target_elem_single = elem
+                break
+
+        # if there is at least one referece, we can assume that the sentence refers to earthquaqe events
+        if single_keyword_flag:
+            # return {'earthquaqe_event': [True, target_elem_single]}
+            return {"event":"earthquake"}
+
+        # otherwise we examine for 2grams multi-word straight patterns and embeddings
+        earthquaqe_keywords_multi = []
+
+        # check 2-grams
+        for i in range(len(parsed_sentence) - 1):
+            bigram = parsed_sentence[i:i + 2].text.lower()
+
+            # case of straight matching
+            straight_matching_multi = straight_pattern_matching(word.text)
+
+            if straight_matching_multi:
+                # return {'earthquaqe_event': [True, straight_matching_multi]}
+                return {"event": "earthquake"}
+
+            # if no straight matching then perform embeddings
+            else:
+                earthquaqe_keywords_multi.append(
+                    compute_similarity_earthquake(bigram, earthquake_multi_embeddings, excluded_keywords))
+
+        # case that the straight multi matching did not give any output
+        multi_keyword_flag = False
+
+        # check until you find one such reference and then break
+        for elem in earthquaqe_keywords_multi:
+            if elem:
+                multi_keyword_flag = True
+                target_elem_multi = elem
+                break
+
+        # if there is at least one referece, we can assume that the sentence refers to earthquaqe events
+        if multi_keyword_flag:
+            # return {'earthquaqe_event': [True, target_elem_multi]}
+            return {"event":"earthquake"}
+
+        # otherwise there is no reference
+        else:
+            return (0, 'EARTHQUAKE_EVENT', 'no_earthquake_reference')
+
+    except:
+        return (0, 'EARTHQUAKE_EVENT', 'unknown_error')